\r\n\r\n \r\n\r\nThis work has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 634476 for project with acronym TREASURE. The content of this book reflects only the authors\' view and the European Union Agency is not responsible for any use that may be made of the information it contains.\r\n',isbn:"978-1-78985-408-4",printIsbn:"978-1-78985-407-7",pdfIsbn:"978-1-83962-011-9",doi:"10.5772/intechopen.83749",price:139,priceEur:155,priceUsd:179,slug:"european-local-pig-breeds-diversity-and-performance-a-study-of-project-treasure",numberOfPages:318,isOpenForSubmission:!1,isInWos:1,hash:"182fe65256f9a0bbc25b0b7576412b0e",bookSignature:"Marjeta Candek-Potokar and Rosa M. 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1. Introduction
We present here our contribution to the "Machine Learning for Bioacoustics" workshop technical challenge of 30th International Conference on Machine Learning (ICML 2013). The aim is to build a classifier able to recognize bird species one can hear from a recording in the wild.
The method we present here is a rather simple strategy for bird songs and calls classification. It builds on known and efficient technologies and ideas and must be considered as a baseline on this challenge. As we are also co-organizing this challenge, our participation aimed at defining a baseline system, with raw features, that all other participants could compare too. We did not look for optimizing each parameter of our system, and as any other participant, we conducted all the modeling and experimentation applying strictly the rules of the challenge. The method we present is dedicated to the particular setting of the challenge. It relies in particular on the fact that training signals are monolabel, i.e. only one species may be heard, while test signals are multilabeled.
2. Description of the method
We present now the main steps of our approach. The Figures 1 and 2 illustrates the main steps of the preprocessing and of feature extraction.
We consider we want to learn a multilabel classifier from a set of N monolabeled training samples {(xi, yi) | i = 1.. N} where each input xi is an audio recording and each yi is a bird species ∀ i,yi\n\t\t\t\t∈ { bu | u = 1.. K} (in our case there are 35 species, K=35). The system should be able to infer the eventually multiple classes (presence of bird species) in a test recording x.
Figure 1.
Main steps of the preprocesing and of feature extraction.
2.1. Preprocessing
Our preprocessing is based on MFCC cepstral coefficients, which have been proved useful for speech recognition [4, 11]. A signal is first transformed into a series of frames where each frame consists in 17 MFCC (mel-frequency cepstral coefficients) feature vectors, including energy. Each frame represents a short duration (e.g. 512 samples of a signal sampled at 44.1 kHz).
2.2. Windowing, silence removal and feature extraction
2.2.1. Windowing
We use windowing, i.e. computing a new feature vector on a window of n frames, to get new feature vectors that are representative of longer segments. The idea is close to the standard syllable extraction step that is used in most of methods for bird identification [12, 2, 1], but is much simpler to implement. In our case we considered segments of about 0.5 second duration (i.e. n ~ few hundreds of frames) and used a sliding window with overlap (about 80%).
2.2.2. Silence removal
We first want to remove segments (windows) corresponding to silence since these would perturbate the training and test steps. This is performed with a clustering step (learnt on training signals) that only considers the average energy of the frames in a window. Ideally this clustering makes that the windows are clustered into silence segments on the one hand, and calls and songs segments on the other hand. Each window with low average energy is considered a silence window and removed from consideration. Our best results were achieved when performing a clustering in three clusters and removing all windows in the lowest energy cluster.
2.2.3. Feature extraction
The final step of the preprocessing consists in computing a reduced set of features for any remaining segment / window. Recall that each segment consists in a series of n 17-dimensional feature vectors (with n in the order of hundreds). Our feature extraction consists in computing 6 values for representing the series of n values for each of the 17 MFCC features. Let consider a particular MFCC feature v, let note (vi)i=1..n, the n values taken by this feature in the n frames of a window and let note vi the mean value of vi. Moreover let note d and D the velocity and the acceleration of v, which are approximated all along the sequences with di=vi+1-vi, and Di=di+1-di. The six features we compute are defined as:
f1=∑i=1n(|vi|)nE1
f2=1n−1∑i=1n(v−v¯i)2E2
f3=1n−2∑i−1n(d−d¯i)2E3
f4=1n−3∑i−1n(D−D¯i)2E4
f5=∑i=1n−1|di|n−1E5
f6=∑i=1n−2|Di|n−2E6
At the end a segment in a window is represented as the concatenation of the 6 above features for the 17 cepstral coefficients. It is then a new feature vector St (with t the number of the window) of dimension 102.
Each signal is finally represented as a sequence of feature vectors St, each representing duration of about 0.5 second with 80% overlap.
2.3. Training
Based on the feature extraction step we described above the simplest strategy to train a classifier (e.g. we used Support Vector Machines) on the feature vectors St which are long enough to include a syllable or a call, with the idea of aggregating all the results found on the windows of a test signal to decide which species are present (see section Inference below).
Yet we found that a better strategy was to first perform a clustering in order to split all samples (i.e. St) corresponding to a species into two different classes. The rationale behind this process is that calls and songs of a particular species are completely different sounds [9] so that corresponding feature vectors St probably lie in different areas in the feature space. It is then probably worth using this prior to design classifiers (hopefully linear) with two times the number of species rather than using non linear classifiers with as many classes as there are species.
We implemented this idea by clustering all the frames St for a given species into two or more clusters. The two clusters are now considered as two classes that correspond to a single species. At the end, a problem of recognizing K species in a signal turns into a classification problem with 2 x K classes. Note also that since the setting of the challenge is such that there is only one species per training signal, all feature vectors St of all signal of a given bird species bu that fall into cluster one are labeled as belonging to class bu1 and all that fall into cluster 2 are labeled as belonging to class bu2.
The final step is to learn a multiclass classifier (SVM) in a one-versus-all fashion, i.e. learning one SVM to classify between the samples from one class and the samples from all other classes. This is a standard approach (named Binary Relevance) for dealing with multilabel classification problem where one sample may belong to multiple classes. It is the optimal method with respect to the Hamming Loss, i.e. the number of class prediction errors (either false positive and false negative).
2.4. Inference
At test time an incoming signal is first preprocessed as explained before in section 2.1, silence windows are removed (using clusters), and feature extraction is performed for all remaining segments. This yields that an input signal is represented as a series of m feature vectors St.
All these feature vectors are processed by all 2K binary SVMs which provide scores that are interpreted as class posterior probabilities (we use a probabilistic version of SVM), we then get a matrix m x 2K of scores P (c |St) with c∈buj|u=1..K,j=1,2 and t = 1..m.
We experimented few ways to aggregate all these scores into a set of K scores, one for each species, enabling ranking the species by decreasing probability of occurrence. Indeed this is the expected format of a challenge submission, from which an AUC (Area Under the Curve) score is computed. First we compute 2K scores, one for each class, then we aggregate the scores of the two classes of a given species.
Our best results were obtained by computing mean probabilities of all scores { P (c | st) | t=1..m } for each class c, using harmonic mean or trimmed harmonic mean (where a percentage of the lowest scores are discarded before computing the mean). This yields scores that we consider as class posterior probabilities of classes given the input signal x, P (c | x).
The ultimate step consists in computing a score for each species bu given the scores of the two corresponding classes bu1 and bu2. We used the following aggregation formulae:
Pbux=1-1-Pbu1x×(1-P(bu2|x))E7
3. Experiments
3.1. Dataset
We describe now the data used for the "Machine Learning for Bioacoustics" technical challenge. Note that the training dataset (signals with corresponding ground truth) was available for learning systems all along the challenge together with the test set, without ground truth. Participants were able to design their methods and select their best models by submitting predictions on the test set which were scores on a subset only of the test set (33%). The final evaluation and the ranking of participants were performed on the full test set once all participants have selected 5 of all their systems submitted.
Training data consisted in thirty-five 30-seconds audio recordings labeled with a single species; there was one recording per species (35 species overall). Yet, some train recording can include low signal-to-noise ratio (SNR) signals of a second bird species of bird. Moreover, according to circadian rhythm of each species, other acoustically actives species of animals can be present such as nocturnal and diurnal insects (Grylidae, Cicada).
Test data consisted in ninety 150-seconds audio recordings with possibly none or multiple species occurring in each signal.
The training and test data recordings have been performed with various devices in various geographical and climatological settings. In particular background and SNR are very different between training and test. All wav audio recordings have been sampled at 44 100 Hz with a 16-bits quantification resolution. Recordings were performed with 3 Song Meter SM2+ (Wildlife Acoustic recording device). Each SM2+ has been installed in a different sector (A, B and C) of a Regional Park of the Upper Chevreuse Valley.
Every SM2+ recorded, at the same dates and hours (between 24 03 2009 and 22 05 2009), one 150-seconds recording per day between 04h48m00s a.m. and 06h31m00s a.m., which correspond to the maximal acoustical bird-activity period.
3.2. Implementation details
3.2.1. Frames and overlapping sizes
We computed Mel-frequency cepstral coefficients (MFCC) with the melfcc.m Matlab function from ROSA laboratory of Columbia University [8]. This function proposes 17 different input parameters. We tested numerous possible configurations [7] and measured for each one the difference of energy contained in a given train file and a reconstructed signal of this recording based on cepstral coefficients.
The difference was minimal with following parameters values:
This process transforms a 30-seconds train audio recording (at 44 kHz sampling rate) into about 7 700 frames of 16 cepstral coefficients which we augmented with the energy computed by setting useenergy=0.
Next we computed feature vector St on 0.5 second windows with 80% overlap, which yields about n=300 feature vectors per training signal (hence per species since there is only one training recording per species) and about m= feature vectors per test signal.
Figure 2.
Technical principle of our best-scored run
3.2.2. LIBSVM settings
We used a multiclass SVM algorithm based on LIBSVM [3]. We selected model parameters (kernel type etc.) through two fold cross validation. Best scores have been obtained with C-SVC SVM type and linear kernel function.
4. Results
4.1. General results
We report only our best results that correspond to the method presented in this paper for various computations for the class score at inference time.
Table 1 shows how the way the mean score of a class is computed on the test set (see section 2.4) and influences the final result. The table compares arithmetic mean, harmonic mean, and trimmed arithmetic mean (at 10, 20 et 30%). A trimmed mean at p% is the arithmetic mean computed after discarding p% extreme values, i.e. the p/2% lowest values and the p/2% largest values.
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Table 1.
Score Kaggle icml (AUC) according to the way scores are aggregated. Public scores are calculated on a third of the test data, while private scores are calculated on the other part. Only the private scores are the official competition results. The best private score of all challengers is 0.694 [13].
Although our method is simple it reached the fourth rank over more than 77 participating teams at the Kaggle ICML Bird challenge with a score of 0.64639 while the best score (Private score) of all challengers was 0.694 (the corresponding public Leaderboard score was 0.743). See [13] for the best system, and [14] for the description of the other systems. It is also worth noting that our system ranked about fifteen only on the validation set (one third of the total test set). This probably shows that our system being maybe simpler than other methods exhibits at the end a more robust behavior and improved generalization ability.
4.2. Monospecific results
According to these scores for 7 species, we notice in Figure 3:
Scores of our model are close to the best ones and evolve the same way for the concerned species. The slight difference is probably due to the way we calculate (trimmed mean) the presence probability of one given species in a 150-seconds recording compared to the presence probability of this same species in a half-second frame.
All teams were not able to score high for Columba palumbus (Common Wood-pigeon), Erithacus rubecula (European Robin), Parus caeruleus (Blue Tit), Parus palustris (Marsh Tit), Pavo cristatus (Blue Peafowl) and Turdus viscivorus (Mistle Thrush).
In the Common Wood-pigeon (top of Figure 4) train recording, we can see a series of 5 syllables (around 500 Hz). Syllables are very stable and different. Their alternation in time domain is strict. Also, the train recording is highly corrupted by cicadas between 4 and 6 kHz and in the test recording, SNR is low. The series last 2.5 seconds (compared to 4 seconds in TRAIN) and are composed of 6 syllables well differentiated.
The European Robin (bottom of Figure 4) is typically bird species whose songs are diverse and rich in syllables. Frequency-domain variability between different songs and syllables is important. Song duration varies between 1.5 and 3 seconds. It is one of the rare species that can emit up to 8 kHz.
In Blue Tit train recording, other species of birds are present. Therefore, Blue Tit produces 5 different cries composed of 5 different syllables.
Mistle Thrush train recording songs vary a lot and are very different from songs in the test recordings.
MFCC compression has the property of lowering the weights of cepstral coefficients corresponding to higher frequencies of the spectrogram. As a result, MFCC can lead to losing a part of the signal that may be important in European Robin\'s case. Futhermore, the high variability of the cries or songs of the different species is difficult to manage by classifiers, especially when they are constrained to retain and learn only 2 types of emissions per species. Considering two types of emissions was particularly sub-optimal for these 3 cases.
Figure 3.
Mean Average Precision (MAP) scores on nine species (ordered in abscissa from left to right) of the 6 best teams of the challenge. The label \'USTVetal\' refers to our team (MAP was not the official metrics of the challenge but give interesting comparisons).
For all teams, scores were very satisfactory for Parus major (Great Tit), Troglodytes troglodytes (Winter Wren) and Turdus merula (Eurasian Blackbird).
Great Tit\'s signals (middle of Figure 4) are very simple and periodically repeated. A 500-hertz high-pass filter has been applied on the train recording.
Winter Wren\'s acoustic patterns are really stable. A 1000-hertz high-pass filter has been applied on the train recording.
Eurasian Blackbird\'s train recording has been filtered by a band pass filter from 1-6 kHz. Best Mean Average Precisions were obtained when low frequency and high-frequency noise was removed by filtering.
Figure 4.
Time-Frequential spectrograms of train recording\'s extracts. From top to bottom: Common Wood-pigeon, Great Tit and European Robin.
We assume that congruence observed between the scores of the 6 best teams for these 9 species is the same considering each of the species. The fact that the scores of each species evolve the same way indicates that the Mean Average Precision (M.A.P) differences between species can be due to:
Some species produce sounds harder to characterize than others: strong variability in frequency and/or temporal domain.
Train recordings can\'t be compared to test recordings regarding SNR: filters, harmonic richness, source-microphone distances etc. differ a lot.
Signals of interest are easier to extract in some train recordings than in others because of data acquisition. Some filters have been applied to a part of the train recordings.
For a given species, the signals provided in the train recording may not include a global repertoire and this way not be part of the respective species test recordings.
For each species, frequency content of emissions and location of source in its environment differ widely. Each bird species uses the available space in an ecosystem differently. Obstacles between source and microphone depend on diet and customs of species (arboricol, walking, granivorous, insectivorous species etc). But all frequencies aren\'t affected the same way by transmission loss in the environment. For example, low frequencies are particularly well filtered by vegetation close from the ground. Common Wood-pigeon typically emits in low frequencies (see figure 4).
Natural (rain, wind, insects) or anthropic (motors etc) acoustic events are more diverse and strong (regarding energy) in test recordings than in train. In addition, these events vary much from one species to an other.
Hence, it seems reasonable to affirm that more complex syllables extraction methods (segmentation step) combined with the MFCC way constitute a better solution to improve our performance. They would allow us to retain intraspecific variability for each class and eliminate non-relevant information.
5. Conclusion and perspectives
Although the method that we presented is simple it performed well on the challenge and was much robust between validation step and test set. We believe this robustness comes from the simplicity of the method that do not rely on complex processing steps (like identifying syllables) that other participants could have used [10, 13, 15, 16].
Possible improvements would consist in the integration in the model of additional information such as syllables extraction, weather condition, or a taxonomia of species, allowing more accurate hierarchical classification schemes. Also the MFCC shall be replaced either by a scattering transform [17] or a deep convolutional network [18], that build invariant, stable and informative signal representations for classification.
Acknowledgments
We thank Dr. Xanadu Halkias for her useful comments on this paper. This work is supported by the MASTODONS CNRS project Scaled Acoustic Biodiversity SABIOD and the Institut Universitaire de France that supports the “Complex Scene Analysis” project. We thank F. Jiguet and J. Sueur and F. Deroussen [6, 5] who provided the challenge data.
PhD funds of 1st author are provided by Agence De l\'Environnement et de la Maîtrise de l\'Energie (mila.galiano@ademe.fr) and by BIOTOPE company (Dr Lagrange, hlagrange@biotope.fr, R&D Manager).
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Introduction",level:"1"},{id:"sec_2",title:"2. Description of the method",level:"1"},{id:"sec_2_2",title:"2.1. Preprocessing",level:"2"},{id:"sec_3_2",title:"2.2. Windowing, silence removal and feature extraction",level:"2"},{id:"sec_3_3",title:"2.2.1. Windowing",level:"3"},{id:"sec_4_3",title:"2.2.2. Silence removal",level:"3"},{id:"sec_5_3",title:"2.2.3. Feature extraction",level:"3"},{id:"sec_7_2",title:"2.3. Training",level:"2"},{id:"sec_8_2",title:"2.4. Inference",level:"2"},{id:"sec_10",title:"3. Experiments",level:"1"},{id:"sec_10_2",title:"3.1. Dataset",level:"2"},{id:"sec_11_2",title:"3.2. Implementation details",level:"2"},{id:"sec_11_3",title:"3.2.1. Frames and overlapping sizes",level:"3"},{id:"sec_12_3",title:"3.2.2. LIBSVM settings",level:"3"},{id:"sec_15",title:"4. Results",level:"1"},{id:"sec_15_2",title:"4.1. General results",level:"2"},{id:"sec_16_2",title:"4.2. Monospecific results",level:"2"},{id:"sec_18",title:"5. Conclusion and perspectives",level:"1"},{id:"sec_19",title:"Acknowledgments",level:"1"}],chapterReferences:[{id:"B1",body:'F. Briggs, X. Fern, and R. Raich. Acoustic classification of bird species from syllables : an empirical study. Technical report, Oregon State University, 2009.'},{id:"B2",body:'F. Briggs, B. Lakshminarayanan, L. Neal, X. Fern, R. Raich, M. Betts, S. Frey, and A. Hadley. Acoustic classification of multiple simultaneous bird species : a multi-instance multi-label approach. Journal of the Acoustical Society of America, 2012.'},{id:"B3",body:'C.-C. Chang. Libsvm. http://www.csie.ntu.edu.tw/~cjlin/libsvm/, 2008.'},{id:"B4",body:'L. Chang-Hsing, L. Yeuan-Kuen, and H. Ren-Zhuang. Automatic recognition of bird songs using cepstral coefficients. Journal of Information Technology and Applications Vol. 1, pp.17-23, 2006.'},{id:"B5",body:'F. Deroussen. Oiseaux des jardins de france. Nashvert Production, Charenton, France, 2001. naturophonia.fr.'},{id:"B6",body:'F. Deroussen and F. Jiguet. Oiseaux de france, les passereaux, 2011.'},{id:"B7",body:'O. Dufour, H. Glotin, T. Artières, and P. Giraudet. Classification de signaux acoustiques : Recherche des valeurs optimales des 17 paramètres d\'entrée de la fonction melfcc. Technical report, Laboratoire Sciences de l’Information et des Systèmes, Université du Sud Toulon Var, 2012.'},{id:"B8",body:'D. P. W. Ellis. PLP and RASTA (and MFCC, and inversion) in Matlab, 2005. online web resource.'},{id:"B9",body:'S. Fagerlund. Acoustics and physical models of bird sounds. In Seminar in acoustics, HUT, Laboratory of Acoustics and Audio Signal Processing, 2004.'},{id:"B10",body:'H. Glotin and J. Sueur. Overview of the first international challenge on bird classification, 2013. online web resource.'},{id:"B11",body:'A. Michael Noll. Short-time spectrum and cepstrum techniques for vocal-pitch detection. Journal of the Acoustical Society of America, Vol. 36, No. 2, pp. 296-302, 1964.'},{id:"B12",body:'L. Neal, F. Briggs, R. Raich, and X. Fern. Time-frequency segmentation of bird song in noisy acoustic environments. In International Conference on Acoustics, Speech and Signal Processing, 2011.'},{id:"B13",body:'Rafael Hernandez Murcia, “Bird identification from continuous audio recordings”, in Proc. of first int. wkp of Machine Learning for Bioacoustics ICML4B joint to ICML 2013, Ed. H. Glotin et al, Atlanta, ISBN 979-10-90821-02-6 http://sis.univ-tln.fr/~glotin/ICML4B2013_proceedings.pdf'},{id:"B14",body:'H. Glotin, Y. Lecun, P. Dugan, C. Clark, X. Halkias, Proceedings of the first int. wkp of Machine Learning for Bioacoustics ICML4B joint to ICML 2013, Ed. H. Glotin et al, Atlanta, ISBN 979-10-90821-02-6 http://sis.univ-tln.fr/~glotin/ICML4B2013_proceedings.pdf'},{id:"B15",body:'Briggs et al., “ICML 2013 Bird Challenge – Tech Report, in Proc. of first int. wkp of Machine Learning for Bioacoustics ICML4B joint to ICML 2013, Ed. H. Glotin et al, Atlanta, ISBN 979-10-90821-02-6 http://sis.univ-tln.fr/~glotin/ICML4B2013_proceedings.pdf'},{id:"B16",body:'Dan Stowell and Mark D. Plumbley, ” Acoustic detection of multiple birds in environmental audio by Matching Pursuit”,in Proc. of first int. wkp of Machine Learning for Bioacoustics ICML4B joint to ICML 2013, Ed. H. Glotin et al, Atlanta, ISBN 979-10-90821-02-6 http://sis.univ-tln.fr/~glotin/ICML4B2013_proceedings.pdf'},{id:"B17",body:'J. Andén and S. 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Université du Sud Toulon Var, France
Aix-Marseille Université, CNRS, ENSAM, LSIS, Marseille, France
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Escobar Barrios, José R. Rangel Méndez, Nancy V. Pérez Aguilar, Guillermo Andrade Espinosa and José L. Dávila Rodríguez",authors:[{id:"12709",title:"Dr.",name:"Jose Rene",middleName:null,surname:"Rangel-Mendez",fullName:"Jose Rene Rangel-Mendez",slug:"jose-rene-rangel-mendez"},{id:"12711",title:"Dr.",name:"Vladimir Alonso",middleName:null,surname:"Escobar Barrios",fullName:"Vladimir Alonso Escobar Barrios",slug:"vladimir-alonso-escobar-barrios"},{id:"112164",title:"Dr",name:"Guillermo",middleName:null,surname:"Andrade-Espinosa",fullName:"Guillermo Andrade-Espinosa",slug:"guillermo-andrade-espinosa"},{id:"112165",title:"Dr.",name:"José Luis",middleName:null,surname:"Dávila-Rodríguez",fullName:"José Luis Dávila-Rodríguez",slug:"jose-luis-davila-rodriguez"},{id:"112167",title:"Dr.",name:"Nancy Verónica",middleName:null,surname:"Pérez-Aguilar",fullName:"Nancy Verónica Pérez-Aguilar",slug:"nancy-veronica-perez-aguilar"}]},{id:"36175",title:"Preparation and Characterization of PVDF/PMMA/Graphene Polymer Blend Nanocomposites by Using ATR-FTIR Technique",slug:"preparation-and-characterization-of-pvdf-pmma-graphene-polymer-blend-nanocomposites-by-using-ft-ir-t",signatures:"Somayeh Mohamadi",authors:[{id:"108556",title:"Dr.",name:"Somayeh",middleName:null,surname:"Mohamadi",fullName:"Somayeh Mohamadi",slug:"somayeh-mohamadi"}]},{id:"36176",title:"Reflectance IR Spectroscopy",slug:"fundamental-of-reflectance-ir-spectroscopy",signatures:"Zahra Monsef Khoshhesab",authors:[{id:"111629",title:"Dr.",name:"Zahra",middleName:null,surname:"Monsef Khoshhesab",fullName:"Zahra Monsef Khoshhesab",slug:"zahra-monsef-khoshhesab"}]},{id:"36177",title:"Evaluation of Graft Copolymerization of Acrylic Monomers Onto Natural Polymers by Means Infrared Spectroscopy",slug:"evaluation-of-graft-copolymerization-of-acrylic-monomers-onto-natural-polymers-by-means-infrared-spe",signatures:"José Luis Rivera-Armenta, Cynthia Graciela Flores-Hernández, Ruth Zurisadai Del Angel-Aldana, Ana María Mendoza-Martínez, Carlos Velasco-Santos and Ana Laura Martínez-Hernández",authors:[{id:"37761",title:"Prof.",name:"Ana Laura",middleName:null,surname:"Martinez-Hernandez",fullName:"Ana Laura Martinez-Hernandez",slug:"ana-laura-martinez-hernandez"},{id:"107855",title:"Dr.",name:"Jose Luis",middleName:null,surname:"Rivera Armenta",fullName:"Jose Luis Rivera Armenta",slug:"jose-luis-rivera-armenta"},{id:"108894",title:"MSc.",name:"Cynthia Graciela",middleName:null,surname:"Flores-Hernández",fullName:"Cynthia Graciela Flores-Hernández",slug:"cynthia-graciela-flores-hernandez"},{id:"108896",title:"MSc.",name:"Ruth Zurisadai",middleName:null,surname:"Del Angel Aldana",fullName:"Ruth Zurisadai Del Angel Aldana",slug:"ruth-zurisadai-del-angel-aldana"},{id:"108898",title:"Dr.",name:"Carlos",middleName:null,surname:"Velasco-Santos",fullName:"Carlos Velasco-Santos",slug:"carlos-velasco-santos"},{id:"108905",title:"Dr.",name:"Ana Maria",middleName:null,surname:"Mendoza-Martínez",fullName:"Ana Maria Mendoza-Martínez",slug:"ana-maria-mendoza-martinez"}]},{id:"36178",title:"Applications of FTIR on Epoxy Resins - Identification, Monitoring the Curing Process, Phase Separation and Water Uptake",slug:"applications-of-ftir-on-epoxy-resins-identification-monitoring-the-curing-process-phase-separatio",signatures:"María González González, Juan Carlos Cabanelas and Juan Baselga",authors:[{id:"107857",title:"Prof.",name:"Juan",middleName:null,surname:"Baselga",fullName:"Juan Baselga",slug:"juan-baselga"},{id:"138113",title:"Dr.",name:"María",middleName:null,surname:"González",fullName:"María González",slug:"maria-gonzalez"},{id:"138114",title:"Dr.",name:"Juan C.",middleName:null,surname:"Cabanelas",fullName:"Juan C. Cabanelas",slug:"juan-c.-cabanelas"}]},{id:"36179",title:"Use of FTIR Analysis to Control the Self-Healing Functionality of Epoxy Resins",slug:"use-of-ft-ir-analysis-to-control-the-self-healing-functionality-of-epoxy-resins",signatures:"Liberata Guadagno and Marialuigia Raimondo",authors:[{id:"106836",title:"Prof.",name:"Liberata",middleName:null,surname:"Guadagno",fullName:"Liberata Guadagno",slug:"liberata-guadagno"}]},{id:"36180",title:"Infrared Analysis of Electrostatic Layer-By-Layer Polymer Membranes Having Characteristics of Heavy Metal Ion Desalination",slug:"infrared-analysis-of-electrostatic-layer-by-layer-polymer-membranes-having-characteristics-of-heavy",signatures:"Weimin Zhou, Huitan Fu and Takaomi Kobayashi",authors:[{id:"110384",title:"Dr.",name:"Takaomi",middleName:null,surname:"Kobayashi",fullName:"Takaomi Kobayashi",slug:"takaomi-kobayashi"}]},{id:"36181",title:"Infrared Spectroscopy as a Tool to Monitor Radiation Curing",slug:"infrared-spectroscopy-as-a-tool-to-monitor-radiation-curing",signatures:"Marco Sangermano, Patrick Meier and Spiros Tzavalas",authors:[{id:"112286",title:"Dr.",name:"Spiros",middleName:null,surname:"Tzavalas",fullName:"Spiros Tzavalas",slug:"spiros-tzavalas"},{id:"114382",title:"Prof.",name:"Marco",middleName:null,surname:"Sangermano",fullName:"Marco Sangermano",slug:"marco-sangermano"},{id:"114384",title:"Dr",name:"Patrick",middleName:null,surname:"Meier",fullName:"Patrick Meier",slug:"patrick-meier"}]},{id:"36182",title:"Characterization of Compositional Gradient Structure of Polymeric Materials by FTIR Technology",slug:"characterization-of-compositional-gradient-structure-of-polymeric-materials-by-ft-ir-technology",signatures:"Alata Hexig and Bayar Hexig",authors:[{id:"20867",title:"Dr.",name:"Bayar",middleName:null,surname:"Hexig",fullName:"Bayar Hexig",slug:"bayar-hexig"},{id:"111986",title:"Dr.",name:"Alata",middleName:null,surname:"Hexig",fullName:"Alata Hexig",slug:"alata-hexig"}]},{id:"36183",title:"Fourier Transform Infrared Spectroscopy - Useful Analytical Tool for Non-Destructive Analysis",slug:"fourier-trasform-infrared-spectroscopy-useful-analytical-tool-for-non-destructive-analysis",signatures:"Simona-Carmen Litescu, Eugenia D. Teodor, Georgiana-Ileana Truica, Andreia Tache and Gabriel-Lucian Radu",authors:[{id:"24425",title:"Dr.",name:"Simona Carmen",middleName:null,surname:"Litescu",fullName:"Simona Carmen Litescu",slug:"simona-carmen-litescu"},{id:"24429",title:"Prof.",name:"Gabriel-Lucian",middleName:null,surname:"Radu",fullName:"Gabriel-Lucian Radu",slug:"gabriel-lucian-radu"},{id:"108318",title:"Dr.",name:"Eugenia D.",middleName:null,surname:"Teodor",fullName:"Eugenia D. Teodor",slug:"eugenia-d.-teodor"},{id:"108323",title:"Dr.",name:"Georgiana-Ileana",middleName:null,surname:"Badea",fullName:"Georgiana-Ileana Badea",slug:"georgiana-ileana-badea"},{id:"136337",title:"Ms.",name:"Andreia",middleName:null,surname:"Tache",fullName:"Andreia Tache",slug:"andreia-tache"}]},{id:"36184",title:"Infrared Spectroscopy in the Analysis of Building and Construction Materials",slug:"infrared-spectroscopy-of-cementitious-materials",signatures:"Lucia Fernández-Carrasco, D. Torrens-Martín, L.M. Morales and Sagrario Martínez-Ramírez",authors:[{id:"107401",title:"Dr.",name:"Lucia J",middleName:null,surname:"Fernández",fullName:"Lucia J Fernández",slug:"lucia-j-fernandez"}]},{id:"36185",title:"Infrared Spectroscopy Techniques in the Characterization of SOFC Functional Ceramics",slug:"infrared-spectroscopy-techniques-in-the-characterization-of-sofc-functional-ceramics",signatures:"Daniel A. Macedo, Moisés R. Cesário, Graziele L. Souza, Beatriz Cela, Carlos A. Paskocimas, Antonio E. Martinelli, Dulce M. A. Melo and Rubens M. Nascimento",authors:[{id:"102015",title:"MSc.",name:"Daniel",middleName:null,surname:"Macedo",fullName:"Daniel Macedo",slug:"daniel-macedo"},{id:"112309",title:"MSc",name:"Moisés",middleName:"Romolos",surname:"Cesário",fullName:"Moisés Cesário",slug:"moises-cesario"},{id:"112310",title:"Ms.",name:"Graziele",middleName:null,surname:"Souza",fullName:"Graziele Souza",slug:"graziele-souza"},{id:"112311",title:"MSc.",name:"Beatriz",middleName:null,surname:"Cela",fullName:"Beatriz Cela",slug:"beatriz-cela"},{id:"112312",title:"Prof.",name:"Carlos",middleName:null,surname:"Paskocimas",fullName:"Carlos Paskocimas",slug:"carlos-paskocimas"},{id:"112314",title:"Prof.",name:"Antonio",middleName:null,surname:"Martinelli",fullName:"Antonio Martinelli",slug:"antonio-martinelli"},{id:"112315",title:"Prof.",name:"Dulce",middleName:null,surname:"Melo",fullName:"Dulce Melo",slug:"dulce-melo"},{id:"112316",title:"Dr.",name:"Rubens",middleName:"Maribondo Do",surname:"Nascimento",fullName:"Rubens Nascimento",slug:"rubens-nascimento"}]},{id:"36186",title:"Infrared Spectroscopy of Functionalized Magnetic Nanoparticles",slug:"infrared-spectroscopy-of-functionalized-magnetic-nanoparticles",signatures:"Perla E. García Casillas, Claudia A. Rodriguez Gonzalez and Carlos A. Martínez Pérez",authors:[{id:"104636",title:"Dr.",name:"Perla E.",middleName:null,surname:"García Casillas",fullName:"Perla E. García Casillas",slug:"perla-e.-garcia-casillas"},{id:"112440",title:"Dr.",name:"Carlos A.",middleName:null,surname:"Martínez Pérez",fullName:"Carlos A. Martínez Pérez",slug:"carlos-a.-martinez-perez"},{id:"112441",title:"Dr.",name:"Claudia A.",middleName:null,surname:"Rodriguez Gonzalez",fullName:"Claudia A. Rodriguez Gonzalez",slug:"claudia-a.-rodriguez-gonzalez"}]},{id:"36187",title:"Determination of Adsorption Characteristics of Volatile Organic Compounds Using Gas Phase FTIR Spectroscopy Flow Analysis",slug:"determination-of-adsorption-characteristics-of-volatile-organic-compounds-using-gas-phase-ftir-spect",signatures:"Tarik Chafik",authors:[{id:"107310",title:"Prof.",name:"Tarik",middleName:null,surname:"Chafik",fullName:"Tarik Chafik",slug:"tarik-chafik"}]},{id:"36188",title:"Identification of Rocket Motor Characteristics from Infrared Emission Spectra",slug:"identification-of-rocket-motor-characteristics-from-infrared-emission-spectra",signatures:"N. Hamp, J.H. Knoetze, C. Aldrich and C. Marais",authors:[{id:"112229",title:"Prof.",name:"Chris",middleName:null,surname:"Aldrich",fullName:"Chris Aldrich",slug:"chris-aldrich"},{id:"112232",title:"Prof.",name:"Hansie",middleName:null,surname:"Knoetze",fullName:"Hansie Knoetze",slug:"hansie-knoetze"},{id:"135327",title:"Ms.",name:"Corne",middleName:null,surname:"Marais",fullName:"Corne Marais",slug:"corne-marais"}]},{id:"36189",title:"Optical Technologies for Determination of Pesticide Residue",slug:"optical-technology-for-determination-of-pesticide-residue",signatures:"Yankun Peng, Yongyu Li and Jingjing Chen",authors:[{id:"113343",title:"Prof.",name:"Yankun",middleName:null,surname:"Peng",fullName:"Yankun Peng",slug:"yankun-peng"},{id:"116636",title:"Dr.",name:"Yongyu",middleName:null,surname:"Li",fullName:"Yongyu Li",slug:"yongyu-li"},{id:"116637",title:"Dr.",name:"Jingjing",middleName:null,surname:"Chen",fullName:"Jingjing Chen",slug:"jingjing-chen"}]},{id:"36190",title:"High Resolution Far Infrared Spectra of the Semiconductor Alloys Obtained Using the Synchrotron Radiation as Source",slug:"high-resolution-spectra-of-semiconductor-s-alloys-obtained-using-the-far-infrared-synchrotron-radi",signatures:"E.M. Sheregii",authors:[{id:"102655",title:"Prof.",name:"Eugen",middleName:null,surname:"Sheregii",fullName:"Eugen Sheregii",slug:"eugen-sheregii"}]},{id:"36191",title:"Effective Reaction Monitoring of Intermediates by ATR-IR Spectroscopy Utilizing Fibre Optic Probes",slug:"effective-reaction-monitoring-of-intermediates-by-atr-ir-spectroscopy-utilizing-fibre-optic-probes",signatures:"Daniel Lumpi and Christian Braunshier",authors:[{id:"109019",title:"Dr.",name:"Christian",middleName:null,surname:"Braunshier",fullName:"Christian Braunshier",slug:"christian-braunshier"},{id:"111798",title:"MSc.",name:"Daniel",middleName:null,surname:"Lumpi",fullName:"Daniel Lumpi",slug:"daniel-lumpi"}]}]}]},onlineFirst:{chapter:{type:"chapter",id:"64650",title:"Barriers to Development of Telemedicine in Developing Countries",doi:"10.5772/intechopen.81723",slug:"barriers-to-development-of-telemedicine-in-developing-countries",body:'\n
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1. Introduction
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Increasing population in the developing countries has created more demand of health care. Demand of affordable and quality health care is increasing day by day. Rapid demand at the global level for healthcare management is increasing over the past few decades, increasing emphasis on healthcare quality [1]. People in poor countries have less access of health care and poor have even less access of healthcare services within the country [2]. Assessing the appropriate health care and improving the quality of care have been a serious issue in developing countries [3]. Many times, quality of public health care in developing countries has been neglected and attention is only given to technical aspects than the interpersonal components [4]. The cost of health care in developing countries has always been a crucial issue. Out of pocket expenditure on health care has increased many folds. Catastrophic health expenditure is posing a threat toward a household’s financial ability to maintain its basic needs [5].
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There are many barriers like geographical access, availability, affordability, and acceptability to access the health care in developing countries [6]. These barriers become more problematic to women, children, old, and physically handicapped population. Even though the health service provision and the geographical access have improved, local women may not use the services unless the provided services meet their demands in quality and cultural manners [7].
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To overcome the barriers, healthcare sector is now using telemedicine solutions to increase the reach of its services to population. The mindboggling developments in Information and Communication Technologies (ICT), particularly, the web-based technologies have opened up new possibilities in providing better health care to population. Telemedicine is gradually coming up as a viable policy option for the governments in developing countries [8].
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Telemedicine is the use of electronic communications and information technologies to provide clinical services when participants are at different locations [9]. Telehealth is used to encompass a broader application of technologies to distance education, health promotion, preventive services, consumer outreach, and other applications wherein electronic communications and information technologies are used to support healthcare services. According to WHO, “Telehealth involves the use of telecommunications and virtual technology to deliver health care outside of traditional healthcare facilities” [10].
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In a broader and detailed way, World Health Organization (WHO) defines telehealth as: “The delivery of healthcare services, where distance is a critical factor, by all healthcare professionals using information and communication technologies for the exchange of valid information for diagnosis, treatment, and prevention of disease and injuries, research and evaluation, and for the continuing education of healthcare providers, all in the interests of advancing the health of individuals and their communities” [11].
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Telemedicine is restricted to the use of IT for treatment and medical care whereas telehealth cover a broader area, where IT is used to enable the environment where people can enjoy their life at fullest. Although both these terms carry a different meaning altogether but in developing countries, both these terms are used interchangeably.
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Mobile Health (mHealth) helps in patient education, health promotion, disease self-management, decrease in healthcare costs, and remote monitoring of patients and can improve healthcare delivery for developing countries [12, 13].
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Lots of efforts are being made by governments (policy makers, researchers, and administrators) to develop the telemedicine network across their geographical boundaries but pace of development is slow and acceptance of technology to population is not picking up. Unfortunately, the technology that has been developed to remove or minimize the barriers to the healthcare seeking currently faces lots of barriers itself and its development has not been happening as it was expected by policy makers and researchers.
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It was expected that telemedicine will reduce the burden of hospitals, suffering of patients, out of pocket expenditure, need of transport, hospital fear, and save the time and money of general public. It was also expected that it will increase the quality of care and will develop the trust among patients toward telehealthcare system. We cannot deny the partial development of telemedicine and few success stories in many parts of the world but the leverage, which we have expected from telemedicine is still lacking. Then question arises where is the problem? Why telemedicine is not picking the pace and why it is not becoming popular among service providers as well among the patients.
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Many telemedicine pilot programs have been launched in developing countries in last three decades. Many evaluation studies [14, 15] have been conducted to know the success and failure of telemedicine networks and programs across the globe. Whatever success we see mostly happened in the developed countries but in most of the developing countries, success of telemedicine program is limited. This chapter will explore the various hurdles in the development of telemedicine and its operations in developing countries. Despite many benefits offered by the telemedicine, it has not been utilized fully to serve humanity and is underused [6, 7, 16, 17, 18].
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There are many barriers in the adoption of telemedicine and adoption failure is serious issue, which needs to be discussed and explored. According to a study, about 75% of the telemedicine projects are abandoned or failed outright and called as failed projects and this percentage goes up to 90% in developing countries [19]. Until we are not able to find out, enlist, analyze, and understand the barriers in the deployment and development of telemedicine, we cannot ensure success of telemedicine program. Following crucial barriers are currently working in the field of telemedicine implantation and operation.
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1.1. Barriers to telemedicine programs
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1.1.1. Policy barriers
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For smooth functioning and development of any system, we need to have definite policies and procedures at State and National level. These defined rules, regulations, procedures, and protocols are necessary to help a telemedicine system to run smoothly and safely and ensure that population receive a quality healthcare services. In many developing countries, there are no uniform and standard telemedicine policy, which leads confusion for designing telemedicine-related services, program, and its smooth implementation.
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Many practitioners have fear of malpractice-related legal issues and which prevents them to actively participate and develop telemedicine program. Malpractice liability is an important barrier in the practice of telemedicine services. Certification and credential barriers also de-motivate practitioners. There is no public policy related to telemedicine for the end users, which can ensure privacy, confidentiality, and security of patient’s health information during teleconsultation [8]. There are weak regulatory frameworks related to reimbursement in government as well as in private sectors against the teleconsultation services.
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Because health is a state matter, state government should frame policies, programs, guidelines, and regulations regarding telemedicine practices and also allocate sufficient financial resources for telemedicine development. In few developing countries, telemedicine policy exists but implantation framework is absent [8].
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There is lack of established international framework on telemedicine and also there is little consensus or understanding on uniform international standards for telemedicine practices. Telemedicine provides services across the state, country, and international borders, so there should be, at least, common international understanding on this issue.
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Standardization of both hardware and software, as well as guidelines for practice, would help program managers to overcome interoperability, portability, and security issues [11].
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1.1.2. Organizational structure
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Lack of formal organizational structure to deliver telemedicine services is the biggest barrier for the development of telemedicine services in any country. Because being a hybrid discipline, it needs collaboration with all possible stakeholders at each level of the healthcare delivery system. Lack of collaboration between the stakeholders in the absence of specific policy becomes bottleneck in the development of telemedicine.
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Department of Health and Family Welfare and Department of Information Technology should have a national level formal collaboration to develop a national telemedicine network. There are examples of such collaboration and presence of telemedicine department in few developing countries like India but it is patchy, broken, and not well established [8].
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The absence of structured organization is another barrier in transforming telemedicine-related vision and political will into policies at central level. If there is no such policy, then framing of program related to accomplish those political wills become impossible. Lack of specific time bound and result-oriented programs become difficult to implement and evaluate. Systematic planning of implementation of such telemedicine programs, its concurrent monitoring, and final evaluation demands lots of trained human resources.
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1.2. Lack of accreditation or regulatory bodies
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There is no specificity and standardization in the practice of telemedicine, which poses accreditation issue. Lack of accreditation of telemedicine facilities creates fear among the users as well as providers. Absence of accreditation councils and regulatory bodies leaves telemedicine in isolation. Medical Councils and other health councils should take responsibility to regulate the practice and procedures of telemedicine.
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There is lack of uniformity in telemedicine regulations across the world. In the absence of definite regulatory policy and guidelines, physician has apprehension and fear to practice telemedicine. Medical and health councils of different countries still find that proposed definition of telemedicine has deficiencies. These councils do not consider telemedicine as a new discipline or a new branch of medicine. Regulators consider that telemedicine presents challenges and assume that it is new and unproven. There is no clarity what to be regulated. An enabling regulatory environment is required to ensure appropriate, adequate, and quality delivery of healthcare services [20].
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1.2.1. Lack of team of champions
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Once telemedicine system is deployed and is placed, then there is a need of project champions, who will implement the telemedicine program. The three major champions are clinical champion, IT champion, and telemedicine champion [21]. Success of any telemedicine program depends on these champions but these champions are very few in developing countries, so most of the deployed telemedicine program die very soon after their piloting. There are also deficiencies in the training and job orientation of these champions. In most of the cases, they are not well oriented about their roles and responsibilities.
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1.3. Lack of telemedicine champions
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There is a paucity of dedicated, focused, and visionary telemedicine leaders in developing countries. These leaders are brand ambassadors of telemedicine and are carrying the flag of telemedicine high even in the adverse situations. Whatever telemedicine work, we see in these developing countries, are only due to individual efforts of these telemedicine champions.
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1.4. Lack of clinical champions: physicians
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Training is an import part of skill development and the organizations should develop a training schedule to train health professionals for smooth delivery of telemedicine services [21]. It is very important to provide training to all government officers regularly. Without proper knowledge of IT of government officers, e-governance project will never see the real face of the project [22].
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Most of doctors are not aware about the latest information technology and find difficulty to used modern IT gadgets. There is lack of telemedicine experts in healthcare sectors. There is a need to include few chapters related to telemedicine in Medical education curriculum to sensitize and orient budding doctors to learn the technical part of this discipline. There should be separate telemedicine education secretariat and directorate in Ministry Medical Education like in Ministry of Health care, which will promote the development of telemedicine [8].
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1.5. Lack of paraclinical champions: nurse providers
\n
Telemedicine health services are also assisted or provided by nursing staff but their contribution in telemedicine is not recognized and acknowledged. Role of nursing staff in expansion of telemedicine could be very vital if proper training and guidance is provided to them. Most of the developing countries do not have trained telenursing officers or staff who can contribute in the development of telemedicine network.
\n
There is also lack of proper institutional training program in the course curriculum like traditional nursing courses. Until nursing students are sensitized toward this new technology, they are not going to make carrier in telenursing. Apprehension and fear toward telemedicine can only be removed through providing the knowledge about telemedicine. There should be basic telemedicine nursing lesson in their course curriculum. Telenursing is still a remote concept in the developing countries, where focus is mainly on telemedicine.
\n
\n
\n
1.6. Lack of IT champions: teletechnicians
\n
Telemedicine is a hybrid system, which involves the medical as well as ICT domain for complete understanding of the telemedicine solutions and its delivery. There is a serious lack of such technical persons, who can run day-to-day business of telemedicine. To run any telemedicine system properly, trained technical manpower is required. There is lack of technical champions in the field of telemedicine in India, especially in the field of health care and only voluntary champions here and there are visible.
\n
It is common fact that many provider physicians and clients cannot fix the technical problems arising from computer system and ICT network. So, for a proper and smooth functioning of telemedicine system, we need trained and expert manpower to establish a stable and continuous communication during teleconsultation [25]. Unfortunately, there is serious lack of such trained persons in the system in most of the developing countries.
\n
There are very few institutions in developing countries, which train and develop this special group of technicians. It is very difficult to find a person who has undergone training in Medicine and in Information Technology.
\n
\n
1.6.1. Technological barriers
\n
Technology itself is becoming a barrier in the development of telemedicine in developing countries. High cost of replacing the older technology is not affordable for many stakeholders.
\n
\n
\n
\n
1.7. Rapid upgradation of ICT
\n
Due to rapid advancement of telemedicine technology, many state-of-the-art facilities and equipment (software and hardware) become obsolete and outdated. A complex and often unwieldy technical infrastructure may yield disappointing evaluations until it becomes more ubiquitous and user-friendly [23]. People working with these outdated technologies become demotivated and frustrated and lose interest in providing services through old technology system. Government also finds it difficult to replace, which is easily due to lots of budgetary requirement for newer technology.
\n
Failure of telemedicine network in Madhya Pradesh, India, is an important example, where Indian Space Research Organization (ISRO) sponsored equipment like camera, television sets and other equipment and software were not utilized for a longtime and became outdated and nonfunctional. Repair and replacement of these equipment and software are so costly that government is not willing to get it repaired and whole telemedicine network has collapsed [14].
\n
Time gap between acquiring hardware and development of customized software is so large that by the time software is ready, the hardware becomes obsolete. This mismatch between software and hardware also create a bottleneck in the development of effective telemedicine solution.
\n
\n
\n
1.8. Inadequate ICT infrastructure
\n
Many developing countries have inadequate availability of Information and Communication Technology (ICT) such as computers, Internet network, printers, and electricity for proper implementation and running of telemedicine program. Internet access and power supply are other issues related to failure of telemedicine network in rural and remote locations [14, 18, 24]. One of the important hurdles to effective delivery of telemedicine solution to rural and remote locations in developing countries is incomplete and insufficient ICT infrastructure.
\n
\n
\n
1.9. Initial huge start-up cost of ICT infrastructure
\n
Telemedicine set up can deploy varieties of information and communication technologies (ICTs) for transmitting information through texts, pictures, audios, and videos to a variety of healthcare providers. Cost depends on the type of ICT being used for the start-up. For setting an audio visual ICT platform for teleconsultation needs huge investment. Budgetary constraints become a major barrier in the development of telemedicine network in developing countries [7, 18, 19, 24]. A sustainable financial support is needed to purchase, deploy, operate, and maintain the sophisticated telemedicine platform [19]. Telecommunication expenses, training of service providers and clients, and need for newer ICT platforms require most of the expenditure.
\n
\n
\n
1.10. Low Internet connectivity
\n
Most of the telemedicine applications require a high speed and reliable Internet bandwidth to run smoothly. Tele-surgery, real time tele-ophthalmology, real time tele-radiology, and emergency consultation are some examples of such applications [25]. Unreliable and low wideband Internet pose barriers in smooth delivery of telemedicine service.
\n
For real-time teleconsultations between providers and clients, there is a need for reliable and high speed Internet availability. Internet coverage is still bottleneck in many developing countries, especially in rural and remote areas. Most rural areas do not have the financial capital to independently invest in a broadband network that would provide high-speed Internet to their inhabitants. Telecommunications (“telecom”) companies are the primary providers of high-speed Internet, but they invest very little in rural areas because such investments are not as profitable [26].
\n
Internet connectivity for transmitting patients’ files, records, pictures, and videos are still limited in many areas, including in China, India, Indonesia, the Philippines, and Vietnam [27]. Recently, it has been observed that Internet access is growing and also the cost of Internet is coming down, which is a good sign for the development of telemedicine on developing countries.
\n
\n
1.10.1. Legal barriers
\n
Telemedicine practices has eliminated many physical and emotional barriers to healthcare seeking but have raised many legal and ethical issues, which are normally not encountered during traditional healthcare delivery. Legal considerations are a major obstacle to telemedicine uptake [8, 21, 28].
\n
\n
\n
\n
1.11. Online prescription
\n
There is no legal framework of e-prescription, digital prescription, or mobile-based SMS prescription. Digital prescriptions are not approved and accepted by Medical Council of India (MCI) or any other regulatory authority [8]. Online prescribing policies vary across the countries and across the states within countries.
\n
Concerns have been raised over various issues like whether an appropriate patient-provider relationship has been established, lack of an adequate physical examination of the patient, accuracy of the patient’s history given the self-reporting of the patient over a telehealth connection, and not meeting state medical board licensing requirements [29]. There is no standardized legal framework to protect practitioners as well as clients for online prescriptions in developing countries.
\n
\n
\n
1.12. Malpractice liability
\n
Most of the doctors are afraid of Consumer Protection Act due to malpractice-related issues. There is a lack of specific standard operating procedures (SOPs)/guidelines for the telemedicine practice [8]. Legal issues surrounding patient privacy, safety, security, and confidentiality also play vital role in teleconsultation. Very little information exists on the extent of malpractice liability and telehealth [29]. Medical malpractice-related legal issues should be identified and addressed for smooth practice of telemedicine.
\n
\n
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1.13. Licensing of telemedicine/telehealth service providers
\n
Highly sophisticated, safe, secure, and speedy teleconsultations have reduced the distance barrier in healthcare seeking and have improved the healthcare access. In order to avoid malpractice in telemedicine, healthcare professionals should be specifically trained for telemedicine as they do for traditional medicine [30]. Poor availability of experts and trained professions raises legal implications and warrants licensing of telemedicine providers.
\n
The responsibility of licensing to telemedicine providers falls under the purview of the state licensing councils or boards of a particular country. These policies governing telemedicine and physician licensure vary widely across the country [29].
\n
Licensing ensures that physicians meet academic and clinical competence standards for the telemedicine practice. It protects public from unqualified and substandard physicians and healthcare professorial. Licensing also helps to enforce continuing standards [31].
\n
\n
\n
1.14. Informed consent before teleconsultation
\n
Need for a prior written or verbal informed consent for any telemedicine consultation and treatment misrepresents telemedicine as a different form of service, rather than as a useful tool that enhances diagnostic and treatment services.
\n
Healthcare providers need to have a clear understanding of what their legal and ethical responsibilities are. Similarly, patients must receive the protection of adequate standards of care and know that the person to whom they are entrusting their health has the proper qualifications [31].
\n
The lack of clear-cut legal guidelines, rules, and regulations hinders the telemedicine to improve healthcare access and healthcare quality through information and communication technology [31].
\n
\n
1.14.1. Financial barriers to telemedicine development
\n
Although telemedicine can be leveraged to increase access to care and reduce the cost of care but that is mainly true for the user’s point of view. Story is different if we look from the side of providers or healthcare organizations. For establishing a telemedicine unit, it needs lots of financial investment. It becomes more difficult for the developing countries to allocate huge budget for the investment in telemedicine.
\n
Establishing and operating a “Telemedicine Unit” require purchasing the equipment needed to setup the system at both provider’s and consumer’s end (in the hospital, clinic, or pharmacy); maintaining the equipment; training the physicians and local healthcare workers on the technology; and compensating the physicians. There are many other costs are involve in delivering teleconsultation like payment of Internet and electricity bills, salary of support staff, other recurring costs etc.
\n
These total costs are so high that many proposals of establishing or starting telemedicine program never take off, or even if it starts, it dies soon and cannot sustain on a long-term basis. Many telemedicine pilot projects have failed because of high maintenance cost [14].
\n
The costs of telemedicine are often high in developing countries, because of low awareness between both patients and local healthcare workers, low information technology literacy, and limited access to infrastructure and technology [27]. Telemedicine service providers are generally unable to bear all costs alone and expect government or development partner to support financially for the sustainability of the telemedicine projects.
\n
Most of the telemedicine solutions and programs tend to be government funded, at least in their initial phases. Due to some reasons, if government stops funding, the system becomes unsustainable as there is no alternative business model. So dependency on public support is another financial barrier in the development of telemedicine in developing countries [27].
\n
Cost incurred in purchase, installation, and maintenance of telemedicine services (telemedicine and communication equipment) are very high and do not give proper return on investment (ROI), so there is less economic benefits to the practitioners, which leads to the bankruptcy and closure of many health facilities in rural communities and also prevents further telemedicine expansion to communities needing specialized services [32]. Insurance companies do not reimburse the teleconsultation bills and payments, which further force the practitioners to stop the telemedicine services. Many hospitals and clinics perceive that telemedicine solutions are too expensive to implement.
\n
\n
\n
\n
1.15. Reimbursement and insurance barriers
\n
Reimbursement of telemedicine services has been reported as one of the important barriers in developed countries [17, 22, 33, 34]. When patient avails healthcare services through telemedicine system, insurance claim may not cover the cost of care as it is not delivered through traditional healthcare system. Such discrimination seldom occurs in developing countries, where health insurance is still a rare commodity [30].
\n
\n
1.15.1. Social barriers in the development of telemedicine
\n
Social and culture milieu of the community and society of a particular country also creates lots of barriers in adapting, utilizing, and sustaining telemedicine services. The lack of ICT literacy, awareness, language barriers, and cultural gaps between the service providers and patients etc. are also major factors, which prevent further development and expansion of telemedicine network in developing countries.
\n
\n
\n
\n
1.16. Resistance to change
\n
A lack of support to newer ICT tools has been observed from both parties (providers and users). Several studies have revealed that the resistance to change has been reported toward telemedicine from providers (physicians) as well as from users (clients/patients) for newer technology [14, 19, 25, 33, 35].
\n
\n
\n
1.17. ICT literacy
\n
In developing countries where general literacy is not even adequate, we can imagine the awareness level of population toward ITC literacy. Poor awareness toward modern technologies and their use in delivering health care seems to be a big barrier in developing countries. People in developing countries are not much aware about the benefits offered by telemedicine. Even physicians are short of IT knowledge and not updated. Poor awareness level creates fears and resistance toward ICT technology and create hurdle in the adoption and development of telemedicine. Age also plays an important role. Many older physicians do not feel comfortable dealing with ICT technology. Some patients, particularly older patients, are hesitant about the new technology.
\n
Many healthcare professionals are not comfortable working with computers and modern gadgets and consider technology extra work for them. They also fear that telemedicine may lead to job loss or a reduction in their bedside presence [27, 33].
\n
\n
\n
1.18. Lack of confidence
\n
There is lack of confidence in patients about the outcome of telemedicine. It is difficult for them to believe that machine can provide healthcare demands without visiting physician face to face [25]. This cultural perception and attitude toward newer technology also possess threat to the development of telemedicine. Even many physicians also think that patient consultation and treatment are incomplete without touching the patient and prefer face-to-face consultation than remote consultation through ICT platform. Some medical practitioners do not want to opt telemedicine practice due to the fear of medical indemnity.
\n
Barriers to adoption and sustainability of rural telehealth embody several factors that must be considered when planning, developing, implementing, and evaluating a rural telehealth program [32].
\n
\n
\n
1.19. Industry-oriented telemedicine
\n
There are three players in the telemedicine viz. physicians as service providers, IT Industry as supplier of technology, and public as user. One of the major hurdles of development of telemedicine in developing countries is the passiveness of provider physician and users.
\n
Most of the telemedicine tools and technologies are developed and supplied by the developed countries and they have strong market influence in the developing countries. IT industry people are very active and try to influence policy makers and administrators in the health system to sell their IT technology (telemedicine-related hardware and software). Their focus is only to sell and install the telemedicine tools and equipment and leave the system for the physician to run.
\n
Failure is bound to happen if providers and users are not taken into account while developing the telemedicine platform. For example, in Madhya Pradesh, India, ISRO and top-level administrators at ministry level decided to implement telemedicine solutions across the state but it failed badly as there were no takers at ground level. Physicians were not convinced and adequately trained for newer technology and public as a user was not aware about the benefit of the platform [14].
\n
\n
\n
\n
2. Conclusion
\n
Health care in developing countries is in the midst of a paradigm shift, from a traditional provider-centered, disease-oriented approach to a patient-centered, health-management model. Telemedicine has influenced almost all aspects of healthcare and many success stories have reported the role of telemedicine in improving healthcare access, reducing cost of care, and enhancing the quality of care. Telemedicine could be an important tool in achieving healthcare coordination and reducing healthcare disparities.
\n
Despite of so much development and successful work in the field of telemedicine, it has yet to become integral part of healthcare system. Success of telemedicine only depends when it becomes integral part of healthcare delivery system and not as a stand-alone project. Now, it is time to take telemedicine from pilot mode to routine operational mode in mainstream health services delivery system.
\n
There is tremendous pressure on governments to provide accessible affordable and quality healthcare to its people. Only alternative and innovative methods like telemedicine can help to fulfill this gap. Current status of telemedicine in developing countries is not very satisfactory and passing through a stage of crisis. This chapter has explored the various barriers in the development of telemedicine in developing countries.
\n
These various barriers mentioned above are impeding the speed of expansion of telemedicine in developing countries. It is now time to minimize the abovementioned barriers and remove the bottlenecks for smooth development of telemedicine network across the globe for the betterment of humanity.
\n
\n
Acknowledgments
\n
The author would like to thank the Department of Public Health and Family Welfare Madhya Pradesh and National Health Mission Madhya Pradesh for providing funding support to conduct telemedicine evaluation survey from where experience has been shared here.
\n
Conflict of interest
The author declares that he has no competing interest with anyone in publishing this chapter.
\n
Notes/thanks/other declarations
\n
No other declarations.
\n
\n',keywords:"telehealth, telemedicine, barriers, developing countries, health care",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/64650.pdf",chapterXML:"https://mts.intechopen.com/source/xml/64650.xml",downloadPdfUrl:"/chapter/pdf-download/64650",previewPdfUrl:"/chapter/pdf-preview/64650",totalDownloads:1395,totalViews:1278,totalCrossrefCites:6,dateSubmitted:"June 29th 2018",dateReviewed:"September 28th 2018",datePrePublished:"December 5th 2018",datePublished:null,dateFinished:null,readingETA:"0",abstract:"Affordability, accessibility, availability, and quality of healthcare services have always been a burning issue for the mankind. The issue of health care is always crucial for the governments and countries irrespective of their financial status. Continuous efforts are being made by policy makers, administrators, and researchers to provide quality health care to the people at the cost that they can afford. Developed countries have adopted many alternative tools and technologies to leverage the supply of good health care but quality and cost of health care are still big issues in these countries. Developing countries are far behind in adopting technology to reduce the cost and improve the quality of health care. Telemedicine has emerged as a new hope to remove the bottlenecks in the healthcare seeking. Developing countries have adopted telemedicine technology in a hurry without proper planning and strategy. Despite more than two decades of adapting telemedicine, developing countries have not achieved any significant success in reducing the cost of care or improving the access of care. This chapter has tried to explore the various barriers to the development of telemedicine in developing countries. Proper enlisting and detailing of these barriers will definitely help governments to understand the loopholes and bottlenecks in the implementation of telemedicine and help them to develop appropriate solution.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/64650",risUrl:"/chapter/ris/64650",signatures:"Surya Bali",book:{id:"8332",title:"Telehealth",subtitle:null,fullTitle:"Telehealth",slug:"telehealth",publishedDate:"February 27th 2019",bookSignature:"Thomas F. Heston",coverURL:"https://cdn.intechopen.com/books/images_new/8332.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"217926",title:"Dr.",name:"Thomas F.",middleName:null,surname:"Heston",slug:"thomas-f.-heston",fullName:"Thomas F. Heston"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:null,sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_1_2",title:"1.1. Barriers to telemedicine programs",level:"2"},{id:"sec_1_3",title:"1.1.1. Policy barriers",level:"3"},{id:"sec_2_3",title:"1.1.2. Organizational structure",level:"3"},{id:"sec_4_2",title:"1.2. Lack of accreditation or regulatory bodies",level:"2"},{id:"sec_4_3",title:"1.2.1. Lack of team of champions",level:"3"},{id:"sec_6_2",title:"1.3. Lack of telemedicine champions",level:"2"},{id:"sec_7_2",title:"1.4. Lack of clinical champions: physicians",level:"2"},{id:"sec_8_2",title:"1.5. Lack of paraclinical champions: nurse providers",level:"2"},{id:"sec_9_2",title:"1.6. Lack of IT champions: teletechnicians",level:"2"},{id:"sec_9_3",title:"1.6.1. Technological barriers",level:"3"},{id:"sec_11_2",title:"1.7. Rapid upgradation of ICT",level:"2"},{id:"sec_12_2",title:"1.8. Inadequate ICT infrastructure",level:"2"},{id:"sec_13_2",title:"1.9. Initial huge start-up cost of ICT infrastructure",level:"2"},{id:"sec_14_2",title:"1.10. Low Internet connectivity",level:"2"},{id:"sec_14_3",title:"1.10.1. Legal barriers",level:"3"},{id:"sec_16_2",title:"1.11. Online prescription",level:"2"},{id:"sec_17_2",title:"1.12. Malpractice liability",level:"2"},{id:"sec_18_2",title:"1.13. Licensing of telemedicine/telehealth service providers",level:"2"},{id:"sec_19_2",title:"1.14. Informed consent before teleconsultation",level:"2"},{id:"sec_19_3",title:"1.14.1. Financial barriers to telemedicine development",level:"3"},{id:"sec_21_2",title:"1.15. Reimbursement and insurance barriers",level:"2"},{id:"sec_21_3",title:"1.15.1. Social barriers in the development of telemedicine",level:"3"},{id:"sec_23_2",title:"1.16. Resistance to change",level:"2"},{id:"sec_24_2",title:"1.17. ICT literacy",level:"2"},{id:"sec_25_2",title:"1.18. Lack of confidence",level:"2"},{id:"sec_26_2",title:"1.19. Industry-oriented telemedicine",level:"2"},{id:"sec_28",title:"2. Conclusion",level:"1"},{id:"sec_29",title:"Acknowledgments",level:"1"},{id:"sec_32",title:"Conflict of interest",level:"1"},{id:"sec_29",title:"Notes/thanks/other declarations",level:"1"}],chapterReferences:[{id:"B1",body:'Kurji Z, Premani ZS, Mithani Y. Review and analysis of quality healthcare system enhancement in developing countries. The Journal of the Pakistan Medical Association. 2015;65(7):6\n'},{id:"B2",body:'Peters. Poverty and Access to Health Care in Developing Countries. Annals of the New York Academy of Sciences [Internet]. 2008. Wiley Online Library. Available from: https://nyaspubs.onlinelibrary.wiley.com/doi/epdf/10.1196/annals.1425.011 [Cited: September 16, 2018]\n'},{id:"B3",body:'Reerink IH, Sauerborn R. Quality of primary health care in developing countries: Recent experiences and future directions. International Journal for Quality in Health Care. 1996;8(2):131-139\n'},{id:"B4",body:'Haddad S, Fournier P. Quality, cost and utilization of health services in developing countries. A longitudinal study in Zaïre. Social Science & Medicine. 1995;40(6):743-753\n'},{id:"B5",body:'Puteh SEW, Almualm Y. Catastrophic Health Expenditure Among Developing Countries. Health Systems and Policy Research [Internet]. 2017;4(1). Available from: http://www.hsprj.com/abstract/catastrophic-health-expenditure-among-developing-countries-18514.html [Cited: September 16, 2018]\n'},{id:"B6",body:'Jacobs B, Ir P, Bigdeli M, Annear PL, Van Damme W. Addressing access barriers to health services: An analytical framework for selecting appropriate interventions in low-income Asian countries. Health Policy and Planning. 2012;27(4):288-300\n'},{id:"B7",body:'Chiang C, Adly Labeeb S, Higushi M, Ghardes Mohamed A, Aoyama A. Barriers to the use of basic health services among women in rural southern Egypt (upper Egypt). Nagoya Journal of Medical Science. 2013;75(3-4):225-231\n'},{id:"B8",body:'Bali S. Enhancing the reach of health care through telemedicine: Status and new possibilities in developing countries. In Health Care Delivery and Clinical Science: Concepts, Methodologies, Tools, and Applications, ed. Information Resources Management Association. Accessed December 03, 2018:1382-1397. DOI: 10.4018/978-1-5225-3926-1.ch069\n'},{id:"B9",body:'Telemedicine, Telehealth, and Health Information Technology an ATA Issue Paper Patient Care Health Provider [Internet]. Available from: http://www.who.int/goe/policies/countries/usa_support_tele.pdf [Cited: September 16, 2018]\n'},{id:"B10",body:'WHO. Telehealth [Internet]. WHO. Available from: http://www.who.int/sustainable-development/health-sector/strategies/telehealth/en/ [Cited: September 16, 2018]\n'},{id:"B11",body:'World Health Organization, editor. Telemedicine: Opportunities and developments in member states: Report on the second Global survey on eHealth [Internet]. 2010. Geneva, Switzerland: World Health Organization. 93 p. 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PMCID: PMC322217\n'},{id:"B21",body:'Alghatani KM. Telemedicine Implementation: Barriers and Recommendations. Journal of Scientific Research and Studies.2016;3(7):140-145\n'},{id:"B22",body:'JahangirAlam M. E-governance in Bangladesh: Present problems and possible suggestions for future development. International Journal of Applied Information Systems. 2012;4(8):21-25\n'},{id:"B23",body:'Field MJ. Telemedicine: A Guide to Assessing Telecommunications for Health Care [Internet]. Institute of Medicine. Available from: https://www.nap.edu/read/5296/chapter/7 [Cited: September 16, 2018]\n'},{id:"B24",body:'Hoque MR, Mazmum MFA, Bao Y. e-Health in Bangladesh: Current status, challenges, and future direction. International Technology Management Review. 2014;4(2):87-96\n'},{id:"B25",body:'Hassibian MR, Hassibian S. Telemedicine acceptance and implementation in developing countries: Benefits, categories, and barriers. Razavi International Journal of Medicine. 2016;4(3):1-7\n'},{id:"B26",body:'Perkins A. A Cure to Rural Healthcare Access: Telemedicine, High-Speed Internet, and Local Government [Internet]. Harvard Journal of Law and Technology. Available from: https://jolt.law.harvard.edu/digest/a-cure-to-rural-healthcare-access-telemedicine-high-speed-internet-and-local-government [Cited: September 16, 2018]\n'},{id:"B27",body:'Endeva, Insight A, Hazarika M.Using Telemedicine to Treat Patients in Underserved Areas: Health Telemedicine Case [Internet]. 2017. World Bank Group. Available from: https://www.innovationpolicyplatform.org/system/files/2_%20Health%20Telemedicine%20Case_Jun15.pdf [Cited: September 18, 2018]\n'},{id:"B28",body:'Stanberry B. Telemedicine: Barriers and opportunities in the 21st century. Journal of Internal Medicine. 2000;247(6):615-628\n'},{id:"B29",body:'Common Legal Barriers. Center for Connected Health Policy [Internet]. Available from: http://www.cchpca.org/common-legal-barriers [Cited: September 17, 2018]\n'},{id:"B30",body:'Combi C, Pozzani G, Pozzi G. Telemedicine for developing countries. Applied Clinical Informatics. 2016;7(4):1025-1050\n'},{id:"B31",body:'Daley HA. Telemedicine: The invisible legal barriers to the health care of the future. Annals of Health Law. 2000;9:35\n'},{id:"B32",body:'Alverson DC, Shannon S, Sullivan E, Prill A, Effertz G, Helitzer D, et al. Telehealth in the trenches: Reporting back from the frontlines in rural America. Telemedicine Journal and e-Health. 2004;10(Suppl 2):S-95-S109\n'},{id:"B33",body:'Bishop TF, Press MJ, Mendelsohn JL, Casalino LP. Electronic communication improves access, but barriers to its widespread adoption remain. Health Affairs | Project HOPE. 2013;32(8):1361-1367\n'},{id:"B34",body:'Wootton R, Vladzymyrskyy A, Zolfo M, Bonnardot L. Experience with Low-cost Telemedicine in Three Different Settings. Recommendations Based on a Proposed Framework for Network Performance Evaluation. Glob Health Action [Internet]. 2011;4. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3234078/ [Cited: September 16, 2018]\n'},{id:"B35",body:'Ghani MKA, Jaber MM, Herman NS. Barriers faces telemedicine implementation in the developing countries: Toward building iraqi telemedicine framework. ARPN Journal of Engineering and Applied Sciences. 2015;10(4):6\n'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Surya Bali",address:"surya.cfm@aiimsbhopal.edu.in",affiliation:'
Department of Community and Family Medicine, All India Institute of Medical Sciences Bhopal, India
Telemedicine Centre, All India Institute of Medical Sciences Bhopal, India
'}],corrections:null},book:{id:"8332",title:"Telehealth",subtitle:null,fullTitle:"Telehealth",slug:"telehealth",publishedDate:"February 27th 2019",bookSignature:"Thomas F. Heston",coverURL:"https://cdn.intechopen.com/books/images_new/8332.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"217926",title:"Dr.",name:"Thomas F.",middleName:null,surname:"Heston",slug:"thomas-f.-heston",fullName:"Thomas F. 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To overcome the challenges, we have started a new research group named “Yonezawa Itadakimasu Research Group,” to focus on the development of 3D printing applications for manufacturing food. We have developed Novel jelly foods that are shaped by 3D printed molds. Fused deposition modeling (FDM) 3D printer for food manufacturing makes the 3D printed molds. First step of making 3D printing mold is to print a cast. Then, food grade silicone is poured into the cast to make a mold. This type of 3D printed mold can be used widely, such as making sweets, restaurant menus, and care foods by changing the design depending on the use of application. Secondly, we started to develop 3D food printers. This type of challenge to develop future foods by 3D printing technology may have a major impact on the care food because the looks of foods are important and will be improved by 3D printing.",signatures:"Mai Kodama, Yumiko Takita, Hideaki Tamate, Azusa Saito, Jin\nGong, Masato Makino, Ajit Khosla, Masaru Kawakami and\nHidemitsu Furukawa",authors:[{id:"204615",title:"Ms.",name:"Mai",surname:"Kodama",fullName:"Mai Kodama",slug:"mai-kodama",email:"maikodama@yz.yamagata-u.ac.jp"},{id:"204618",title:"Prof.",name:"Hidemitsu",surname:"Furukawa",fullName:"Hidemitsu Furukawa",slug:"hidemitsu-furukawa",email:"furukawa@yz.yamagata-u.ac.jp"},{id:"204619",title:"Prof.",name:"Masaru",surname:"Kawakami",fullName:"Masaru Kawakami",slug:"masaru-kawakami",email:"kmasaru@yz.yamagata-u.ac.jp"},{id:"204688",title:"Dr.",name:"Jin",surname:"Gong",fullName:"Jin Gong",slug:"jin-gong",email:"jingong@yz.yamagata-u.ac.jp"},{id:"204689",title:"Dr.",name:"Azusa",surname:"Saito",fullName:"Azusa Saito",slug:"azusa-saito",email:"azusasaito@yz.yamagata-u.ac.jp"},{id:"204690",title:"Mr.",name:"Hideaki",surname:"Tamate",fullName:"Hideaki Tamate",slug:"hideaki-tamate",email:"tamate@yz.yamagata-u.ac.jp"}],book:{title:"Future Foods",slug:"future-foods",productType:{id:"1",title:"Edited Volume"}}}],collaborators:[{id:"65501",title:"Prof.",name:"Marcello",surname:"Nicoletti",slug:"marcello-nicoletti",fullName:"Marcello Nicoletti",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Sapienza University of Rome",institutionURL:null,country:{name:"Italy"}}},{id:"143375",title:"Dr.",name:"Noe",surname:"Aguilar Rivera",slug:"noe-aguilar-rivera",fullName:"Noe Aguilar Rivera",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"147822",title:"Prof.",name:"Pierre",surname:"Rougé",slug:"pierre-rouge",fullName:"Pierre Rougé",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"196177",title:"Prof.",name:"Dushimirimana",surname:"Severin",slug:"dushimirimana-severin",fullName:"Dushimirimana Severin",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"196180",title:"Prof.",name:"Thierry",surname:"Hance",slug:"thierry-hance",fullName:"Thierry Hance",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"196181",title:"Dr.",name:"David",surname:"Damiens",slug:"david-damiens",fullName:"David Damiens",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"203783",title:"Dr.",name:"Paola",surname:"Del Serrone",slug:"paola-del-serrone",fullName:"Paola Del Serrone",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Sapienza University of Rome",institutionURL:null,country:{name:"Italy"}}},{id:"207098",title:"Dr.",name:"Régulo Carlos",surname:"Llarena-Hernández",slug:"regulo-carlos-llarena-hernandez",fullName:"Régulo Carlos Llarena-Hernández",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"207099",title:"Dr.",name:"Christian Michel-Cuello",surname:"Michel-Cuello",slug:"christian-michel-cuello-michel-cuello",fullName:"Christian Michel-Cuello Michel-Cuello",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Autonomous University of San Luis Potosí",institutionURL:null,country:{name:"Mexico"}}},{id:"207100",title:"Dr.",name:"Martin Roberto",surname:"Gámez- Pastrana",slug:"martin-roberto-gamez-pastrana",fullName:"Martin Roberto Gámez- Pastrana",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null}]},generic:{page:{slug:"our-story",title:"Our story",intro:"
The company was founded in Vienna in 2004 by Alex Lazinica and Vedran Kordic, two PhD students researching robotics. While completing our PhDs, we found it difficult to access the research we needed. So, we decided to create a new Open Access publisher. A better one, where researchers like us could find the information they needed easily. The result is IntechOpen, an Open Access publisher that puts the academic needs of the researchers before the business interests of publishers.
",metaTitle:"Our story",metaDescription:"The company was founded in Vienna in 2004 by Alex Lazinica and Vedran Kordic, two PhD students researching robotics. While completing our PhDs, we found it difficult to access the research we needed. So, we decided to create a new Open Access publisher. A better one, where researchers like us could find the information they needed easily. The result is IntechOpen, an Open Access publisher that puts the academic needs of the researchers before the business interests of publishers.",metaKeywords:null,canonicalURL:"/page/our-story",contentRaw:'[{"type":"htmlEditorComponent","content":"
We started by publishing journals and books from the fields of science we were most familiar with - AI, robotics, manufacturing and operations research. Through our growing network of institutions and authors, we soon expanded into related fields like environmental engineering, nanotechnology, computer science, renewable energy and electrical engineering, Today, we are the world’s largest Open Access publisher of scientific research, with over 4,200 books and 54,000 scientific works including peer-reviewed content from more than 116,000 scientists spanning 161 countries. Our authors range from globally-renowned Nobel Prize winners to up-and-coming researchers at the cutting edge of scientific discovery.
\\n\\n
In the same year that IntechOpen was founded, we launched what was at the time the first ever Open Access, peer-reviewed journal in its field: the International Journal of Advanced Robotic Systems (IJARS).
\\n\\n
The IntechOpen timeline
\\n\\n
2004
\\n\\n
\\n\\t
Intech Open is founded in Vienna, Austria, by Alex Lazinica and Vedran Kordic, two PhD students, and their first Open Access journals and books are published.
\\n\\t
Alex and Vedran launch the first Open Access, peer-reviewed robotics journal and IntechOpen’s flagship publication, the International Journal of Advanced Robotic Systems (IJARS).
\\n
\\n\\n
2005
\\n\\n
\\n\\t
IntechOpen publishes its first Open Access book: Cutting Edge Robotics.
\\n
\\n\\n
2006
\\n\\n
\\n\\t
IntechOpen publishes a special issue of IJARS, featuring contributions from NASA scientists regarding the Mars Exploration Rover missions.
\\n
\\n\\n
2008
\\n\\n
\\n\\t
Downloads milestone: 200,000 downloads reached
\\n
\\n\\n
2009
\\n\\n
\\n\\t
Publishing milestone: the first 100 Open Access STM books are published
\\n
\\n\\n
2010
\\n\\n
\\n\\t
Downloads milestone: one million downloads reached
\\n\\t
IntechOpen expands its book publishing into a new field: medicine.
\\n
\\n\\n
2011
\\n\\n
\\n\\t
Publishing milestone: More than five million downloads reached
\\n\\t
IntechOpen publishes 1996 Nobel Prize in Chemistry winner Harold W. Kroto’s “Strategies to Successfully Cross-Link Carbon Nanotubes”. Find it here.
\\n\\t
IntechOpen and TBI collaborate on a project to explore the changing needs of researchers and the evolving ways that they discover, publish and exchange information. The result is the survey “Author Attitudes Towards Open Access Publishing: A Market Research Program”.
\\n\\t
IntechOpen hosts SHOW - Share Open Access Worldwide; a series of lectures, debates, round-tables and events to bring people together in discussion of open source principles, intellectual property, content licensing innovations, remixed and shared culture and free knowledge.
\\n
\\n\\n
2012
\\n\\n
\\n\\t
Publishing milestone: 10 million downloads reached
\\n\\t
IntechOpen holds Interact2012, a free series of workshops held by figureheads of the scientific community including Professor Hiroshi Ishiguro, director of the Intelligent Robotics Laboratory, who took the audience through some of the most impressive human-robot interactions observed in his lab.
\\n
\\n\\n
2013
\\n\\n
\\n\\t
IntechOpen joins the Committee on Publication Ethics (COPE) as part of a commitment to guaranteeing the highest standards of publishing.
\\n
\\n\\n
2014
\\n\\n
\\n\\t
IntechOpen turns 10, with more than 30 million downloads to date.
\\n\\t
IntechOpen appoints its first Regional Representatives - members of the team situated around the world dedicated to increasing the visibility of our authors’ published work within their local scientific communities.
\\n
\\n\\n
2015
\\n\\n
\\n\\t
Downloads milestone: More than 70 million downloads reached, more than doubling since the previous year.
\\n\\t
Publishing milestone: IntechOpen publishes its 2,500th book and 40,000th Open Access chapter, reaching 20,000 citations in Thomson Reuters ISI Web of Science.
\\n\\t
40 IntechOpen authors are included in the top one per cent of the world’s most-cited researchers.
\\n\\t
Thomson Reuters’ ISI Web of Science Book Citation Index begins indexing IntechOpen’s books in its database.
\\n
\\n\\n
2016
\\n\\n
\\n\\t
IntechOpen is identified as a world leader in Simba Information’s Open Access Book Publishing 2016-2020 report and forecast. IntechOpen came in as the world’s largest Open Access book publisher by title count.
\\n
\\n\\n
2017
\\n\\n
\\n\\t
Downloads milestone: IntechOpen reaches more than 100 million downloads
\\n\\t
Publishing milestone: IntechOpen publishes its 3,000th Open Access book, making it the largest Open Access book collection in the world
We started by publishing journals and books from the fields of science we were most familiar with - AI, robotics, manufacturing and operations research. Through our growing network of institutions and authors, we soon expanded into related fields like environmental engineering, nanotechnology, computer science, renewable energy and electrical engineering, Today, we are the world’s largest Open Access publisher of scientific research, with over 4,200 books and 54,000 scientific works including peer-reviewed content from more than 116,000 scientists spanning 161 countries. Our authors range from globally-renowned Nobel Prize winners to up-and-coming researchers at the cutting edge of scientific discovery.
\n\n
In the same year that IntechOpen was founded, we launched what was at the time the first ever Open Access, peer-reviewed journal in its field: the International Journal of Advanced Robotic Systems (IJARS).
\n\n
The IntechOpen timeline
\n\n
2004
\n\n
\n\t
Intech Open is founded in Vienna, Austria, by Alex Lazinica and Vedran Kordic, two PhD students, and their first Open Access journals and books are published.
\n\t
Alex and Vedran launch the first Open Access, peer-reviewed robotics journal and IntechOpen’s flagship publication, the International Journal of Advanced Robotic Systems (IJARS).
\n
\n\n
2005
\n\n
\n\t
IntechOpen publishes its first Open Access book: Cutting Edge Robotics.
\n
\n\n
2006
\n\n
\n\t
IntechOpen publishes a special issue of IJARS, featuring contributions from NASA scientists regarding the Mars Exploration Rover missions.
\n
\n\n
2008
\n\n
\n\t
Downloads milestone: 200,000 downloads reached
\n
\n\n
2009
\n\n
\n\t
Publishing milestone: the first 100 Open Access STM books are published
\n
\n\n
2010
\n\n
\n\t
Downloads milestone: one million downloads reached
\n\t
IntechOpen expands its book publishing into a new field: medicine.
\n
\n\n
2011
\n\n
\n\t
Publishing milestone: More than five million downloads reached
\n\t
IntechOpen publishes 1996 Nobel Prize in Chemistry winner Harold W. Kroto’s “Strategies to Successfully Cross-Link Carbon Nanotubes”. Find it here.
\n\t
IntechOpen and TBI collaborate on a project to explore the changing needs of researchers and the evolving ways that they discover, publish and exchange information. The result is the survey “Author Attitudes Towards Open Access Publishing: A Market Research Program”.
\n\t
IntechOpen hosts SHOW - Share Open Access Worldwide; a series of lectures, debates, round-tables and events to bring people together in discussion of open source principles, intellectual property, content licensing innovations, remixed and shared culture and free knowledge.
\n
\n\n
2012
\n\n
\n\t
Publishing milestone: 10 million downloads reached
\n\t
IntechOpen holds Interact2012, a free series of workshops held by figureheads of the scientific community including Professor Hiroshi Ishiguro, director of the Intelligent Robotics Laboratory, who took the audience through some of the most impressive human-robot interactions observed in his lab.
\n
\n\n
2013
\n\n
\n\t
IntechOpen joins the Committee on Publication Ethics (COPE) as part of a commitment to guaranteeing the highest standards of publishing.
\n
\n\n
2014
\n\n
\n\t
IntechOpen turns 10, with more than 30 million downloads to date.
\n\t
IntechOpen appoints its first Regional Representatives - members of the team situated around the world dedicated to increasing the visibility of our authors’ published work within their local scientific communities.
\n
\n\n
2015
\n\n
\n\t
Downloads milestone: More than 70 million downloads reached, more than doubling since the previous year.
\n\t
Publishing milestone: IntechOpen publishes its 2,500th book and 40,000th Open Access chapter, reaching 20,000 citations in Thomson Reuters ISI Web of Science.
\n\t
40 IntechOpen authors are included in the top one per cent of the world’s most-cited researchers.
\n\t
Thomson Reuters’ ISI Web of Science Book Citation Index begins indexing IntechOpen’s books in its database.
\n
\n\n
2016
\n\n
\n\t
IntechOpen is identified as a world leader in Simba Information’s Open Access Book Publishing 2016-2020 report and forecast. IntechOpen came in as the world’s largest Open Access book publisher by title count.
\n
\n\n
2017
\n\n
\n\t
Downloads milestone: IntechOpen reaches more than 100 million downloads
\n\t
Publishing milestone: IntechOpen publishes its 3,000th Open Access book, making it the largest Open Access book collection in the world
\n
\n"}]},successStories:{items:[]},authorsAndEditors:{filterParams:{sort:"featured,name"},profiles:[{id:"6700",title:"Dr.",name:"Abbass A.",middleName:null,surname:"Hashim",slug:"abbass-a.-hashim",fullName:"Abbass A. Hashim",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/6700/images/1864_n.jpg",biography:"Currently I am carrying out research in several areas of interest, mainly covering work on chemical and bio-sensors, semiconductor thin film device fabrication and characterisation.\nAt the moment I have very strong interest in radiation environmental pollution and bacteriology treatment. The teams of researchers are working very hard to bring novel results in this field. I am also a member of the team in charge for the supervision of Ph.D. students in the fields of development of silicon based planar waveguide sensor devices, study of inelastic electron tunnelling in planar tunnelling nanostructures for sensing applications and development of organotellurium(IV) compounds for semiconductor applications. I am a specialist in data analysis techniques and nanosurface structure. I have served as the editor for many books, been a member of the editorial board in science journals, have published many papers and hold many patents.",institutionString:null,institution:{name:"Sheffield Hallam University",country:{name:"United Kingdom"}}},{id:"54525",title:"Prof.",name:"Abdul Latif",middleName:null,surname:"Ahmad",slug:"abdul-latif-ahmad",fullName:"Abdul Latif Ahmad",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"20567",title:"Prof.",name:"Ado",middleName:null,surname:"Jorio",slug:"ado-jorio",fullName:"Ado Jorio",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Universidade Federal de Minas Gerais",country:{name:"Brazil"}}},{id:"47940",title:"Dr.",name:"Alberto",middleName:null,surname:"Mantovani",slug:"alberto-mantovani",fullName:"Alberto Mantovani",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"12392",title:"Mr.",name:"Alex",middleName:null,surname:"Lazinica",slug:"alex-lazinica",fullName:"Alex Lazinica",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/12392/images/7282_n.png",biography:"Alex Lazinica is the founder and CEO of IntechOpen. After obtaining a Master's degree in Mechanical Engineering, he continued his PhD studies in Robotics at the Vienna University of Technology. Here he worked as a robotic researcher with the university's Intelligent Manufacturing Systems Group as well as a guest researcher at various European universities, including the Swiss Federal Institute of Technology Lausanne (EPFL). During this time he published more than 20 scientific papers, gave presentations, served as a reviewer for major robotic journals and conferences and most importantly he co-founded and built the International Journal of Advanced Robotic Systems- world's first Open Access journal in the field of robotics. Starting this journal was a pivotal point in his career, since it was a pathway to founding IntechOpen - Open Access publisher focused on addressing academic researchers needs. Alex is a personification of IntechOpen key values being trusted, open and entrepreneurial. Today his focus is on defining the growth and development strategy for the company.",institutionString:null,institution:{name:"TU Wien",country:{name:"Austria"}}},{id:"19816",title:"Prof.",name:"Alexander",middleName:null,surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/19816/images/1607_n.jpg",biography:"Alexander I. Kokorin: born: 1947, Moscow; DSc., PhD; Principal Research Fellow (Research Professor) of Department of Kinetics and Catalysis, N. Semenov Institute of Chemical Physics, Russian Academy of Sciences, Moscow.\r\nArea of research interests: physical chemistry of complex-organized molecular and nanosized systems, including polymer-metal complexes; the surface of doped oxide semiconductors. He is an expert in structural, absorptive, catalytic and photocatalytic properties, in structural organization and dynamic features of ionic liquids, in magnetic interactions between paramagnetic centers. The author or co-author of 3 books, over 200 articles and reviews in scientific journals and books. He is an actual member of the International EPR/ESR Society, European Society on Quantum Solar Energy Conversion, Moscow House of Scientists, of the Board of Moscow Physical Society.",institutionString:null,institution:{name:"Semenov Institute of Chemical Physics",country:{name:"Russia"}}},{id:"62389",title:"PhD.",name:"Ali Demir",middleName:null,surname:"Sezer",slug:"ali-demir-sezer",fullName:"Ali Demir Sezer",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/62389/images/3413_n.jpg",biography:"Dr. Ali Demir Sezer has a Ph.D. from Pharmaceutical Biotechnology at the Faculty of Pharmacy, University of Marmara (Turkey). He is the member of many Pharmaceutical Associations and acts as a reviewer of scientific journals and European projects under different research areas such as: drug delivery systems, nanotechnology and pharmaceutical biotechnology. Dr. Sezer is the author of many scientific publications in peer-reviewed journals and poster communications. Focus of his research activity is drug delivery, physico-chemical characterization and biological evaluation of biopolymers micro and nanoparticles as modified drug delivery system, and colloidal drug carriers (liposomes, nanoparticles etc.).",institutionString:null,institution:{name:"Marmara University",country:{name:"Turkey"}}},{id:"61051",title:"Prof.",name:"Andrea",middleName:null,surname:"Natale",slug:"andrea-natale",fullName:"Andrea Natale",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"100762",title:"Prof.",name:"Andrea",middleName:null,surname:"Natale",slug:"andrea-natale",fullName:"Andrea Natale",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"St David's Medical Center",country:{name:"United States of America"}}},{id:"107416",title:"Dr.",name:"Andrea",middleName:null,surname:"Natale",slug:"andrea-natale",fullName:"Andrea Natale",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Texas Cardiac Arrhythmia",country:{name:"United States of America"}}},{id:"64434",title:"Dr.",name:"Angkoon",middleName:null,surname:"Phinyomark",slug:"angkoon-phinyomark",fullName:"Angkoon Phinyomark",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/64434/images/2619_n.jpg",biography:"My name is Angkoon Phinyomark. I received a B.Eng. degree in Computer Engineering with First Class Honors in 2008 from Prince of Songkla University, Songkhla, Thailand, where I received a Ph.D. degree in Electrical Engineering. My research interests are primarily in the area of biomedical signal processing and classification notably EMG (electromyography signal), EOG (electrooculography signal), and EEG (electroencephalography signal), image analysis notably breast cancer analysis and optical coherence tomography, and rehabilitation engineering. I became a student member of IEEE in 2008. During October 2011-March 2012, I had worked at School of Computer Science and Electronic Engineering, University of Essex, Colchester, Essex, United Kingdom. In addition, during a B.Eng. I had been a visiting research student at Faculty of Computer Science, University of Murcia, Murcia, Spain for three months.\n\nI have published over 40 papers during 5 years in refereed journals, books, and conference proceedings in the areas of electro-physiological signals processing and classification, notably EMG and EOG signals, fractal analysis, wavelet analysis, texture analysis, feature extraction and machine learning algorithms, and assistive and rehabilitative devices. I have several computer programming language certificates, i.e. Sun Certified Programmer for the Java 2 Platform 1.4 (SCJP), Microsoft Certified Professional Developer, Web Developer (MCPD), Microsoft Certified Technology Specialist, .NET Framework 2.0 Web (MCTS). 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