NIST test results.
\r\n\tDiagnosis and management of complications while on ECMO therapy and weaning to recovery or advanced therapies will be also discussed.
\r\n\r\n\tChapters focusing on specific patient populations, such as cardiogenic shock, thoracic organ transplantation, trauma, and neonates, Covid-19 syndrome, will provide insight into the particular challenges in dealing with the unusual problems of these very diverse groups.
\r\n\r\n\tThe goal of this book is to provide, thanks to the thorough contributions by known experts in the field, a framework for successful program development. Hopefully, this text will also inspire others to further advance this delicate field.
",isbn:"978-1-80356-549-1",printIsbn:"978-1-80356-548-4",pdfIsbn:"978-1-80356-550-7",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,isSalesforceBook:!1,isNomenclature:!1,hash:"254c18981115aeda50bdf71829902141",bookSignature:"Dr. Antonio Loforte",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11718.jpg",keywords:"Heart Failure, Cardiogenic Shock, Respiratory Failure, Circulatory Failure, End-Organ Dysfunction, VA-ECMO, VV ECMO, Central ECMO, ECMO Running, Weaning off ECMO, Adverse Events While on ECMO, Survival on ECMO",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"March 10th 2022",dateEndSecondStepPublish:"April 7th 2022",dateEndThirdStepPublish:"June 6th 2022",dateEndFourthStepPublish:"August 25th 2022",dateEndFifthStepPublish:"October 24th 2022",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"3 months",secondStepPassed:!0,areRegistrationsClosed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Dr. Loforte is a dedicated and pioneering researcher in the surgical treatment of advanced heart failure in terms of LVAD, BVAD, ECLS, and TAH adoption in different clinical scenarios. He is a member of several professional organizations including the prestigious STS, ISHLT, ASAIO, EACTS, RHICS, SICCH, SITO, ELSO, and ESOT among others. His bibliography lists over 150 peer-reviewed original articles, 250 abstracts (communications) for international meetings, 20 book chapters, and 8 manuals.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"42172",title:"Dr.",name:"Antonio",middleName:null,surname:"Loforte",slug:"antonio-loforte",fullName:"Antonio Loforte",profilePictureURL:"https://mts.intechopen.com/storage/users/42172/images/system/42172.jpg",biography:"Dr. Loforte is currently staff surgeon and chair of the Mechanical Circulatory Support (MCS) program at the Department of Cardiothoracic, Transplantation and Vascular Surgery, S. Orsola Hospital, ALMA Mater Studiorum University of Bologna, IRCCS Bologna, Italy. He completed his cardiothoracic surgery recidency at the University of Bologna, S. Orsola Hospital (Italy), at St. Antonius Ziekenhuis, Nieuwegein (the Netherlands) and the Deutsches Herzzentrum Berlin (Germany). He additionally joined the Michael E. DeBakey Department of Surgery, Division of Transplant and Assist Devices, in Houston, Texas, USA.\nDr. Loforte is a member of several professional organizations including the prestigious STS, ISHLT, ASAIO, EACTS, RHICS, SICCH, SITO, ELSO, ESOT among others. His bibliography lists over 150 peer-reviewed original articles, 250 abstracts (communications) for international meetings, 20 book chapters, and 8 manuals. He serves as a reviewer for 25 international journals and is part of the editorial board in 10 of them. He received a ‘European Ph.D. label’ in Organ Transplantation and ten international awards in Europe and USA.",institutionString:"Division of Cardiac Surgery, S. 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For users, it is of potentially great value to associate a personal device with another device in a spontaneous manner, i.e. without need for pre-configuration between two legitimate devices. Pairing devices can be used for the purpose of short-lived interactions, for example, file transfer and synchronization, or aimed at longer lived pairing, for example, pairing a smartphone with accessories.
The wireless nature of the communication between devices gives rise to problems of authentication and security [1]. Figure 1 presents an example of on-body devices with potential adversaries. An adversary can listen to the communication between legitimate devices and eavesdrop private information. Traditional security mechanisms rely on cryptographic keys to support integrity and confidentiality services. Usually, two parties will first establish a common key, and then use the key to encrypt/decrypt subsequent communications between these two devices. In a dynamic mobile environment, mobile devices need to establish point-to-point association frequently. However, it is difficult to ensure the availability of a certificate authority or a key management center. Therefore, it is necessary to have alternative method for key distribution between mobile devices without resorting to a fixed infrastructure.
Legitimate device vs. adversary device.
In current mobile systems, this is achieved by key exchange methods, which are either manual (e.g. typing in the key in a keypad) or exploit key-exchange algorithms. For the first case, a common mechanism for peer device authentication is personal identification number (PIN) code entry by the user into the involved devices [2]. Another example is that the user needs to make sure two devices show the same PIN code on the display when pairing devices using Bluetooth.
However, a primary requirement for human-involved authentication is ease of use and can interact with another device without much user intervention. Such methods offer a reasonably secure way of establishing a common key, but can only be acceptable for occasional use. The number of pairing between wearable devices can be expected to grow considerably as mobile devices are becoming increasingly pervasive. Therefore, the human-involved authentication method is undesirable when users seek to engage in fast and short-lived authentications frequently. To address the problem of pairing devices frequently, one can save the secure key on the device and use it next time. However, this method cannot ensure the security of the key because the stored key can be stolen. On the other hand, due to the small form factor and user interface (UI) of wearable devices, such method is not well suitable because wearable devices are not assumed to include screen and keyboard, e.g. pacemaker and health monitor. For the key-exchange algorithm, a common key exchange method is Diffie-Hellman (DH) protocol which is used to distribute a symmetric key between two parties [3]. However, the premise of DH protocol is that two devices to be paired together are legitimate devices. It cannot be used to distinguish adversary device with legitimate devices.
In this chapter, we propose and implement a shared secret key generation protocol for smart wearable devices based on gait. Gait refers to a person’s manner of walking [4]. The intuition of the proposed key generation protocol is that devices on the same body experience similar signals when the user is walking. Therefore, the similar signals can be used to generate a shared secret key between legitimate devices. The proposed protocol provides an intuitive, unobtrusive method to pair wearable devices when they are on the same body. To the best of our knowledge, this is the first work to generate a key for wearable devices based on gait. The main contributions of this paper are two-fold:
Shared key generation protocol: We present a novel, lightweight key generation protocol for wearable devices based on gait signals. We experimentally demonstrate that the keys generated on two separate wearable devices on the same body can achieve a 100% bit agreement rate. The proposed key generation protocol is able to generate a 128-bit key with entropy varying from 0.93 to 1 by walking 5 s (≈10 steps).
Implementation: We implement the proposed key generation protocol on modern smartphone. We report system overhead such as processing time and power consumption, and demonstrate the feasibility of the proposed protocol for use in contemporary wearable devices.
The rest of the chapter is organized as follows. We first introduce system model in Section 2. Then we specify the shared key generation protocol in Section 3. We evaluate the performance of the proposed key generation protocol in Section 4 and implement the system in Section 5. Finally, Section 6 concludes the chapter.
We envision the use of the proposed system primarily for pairing wearable and implantable devices such as smartphone, smartwatch and pacemaker. Figure 2 illustrates a typical user model: a user wants to read private health information from pacemaker (Bob) through a smart watch (Alice). He walks several steps, and then Alice and Bob generate a shared secret key by exploiting the gait signals. The key is then used to encrypt/decrypt the messages between two parties.
System model.
In this section, we will provide an overview of the proposed scheme. Figure 3 illustrates the flowchart of the key generation protocol. Suppose Alice (smartphone) wants to read data from Bob (pacemaker). Alice first broadcasts a
Signal processing. Signal processing consists of two steps: temporal alignment and spatial alignment. Temporal alignment is used to synchronize acceleration samples at Alice and Bob. Spatial alignment is used to transform the acceleration data to the same coordinate system to facilitate key generation.
Key generation. The key generation phase is composed of three parts: quantization, reconciliation and privacy amplification. In quantization stage, Alice and Bob convert their observed information into bits. In reconciliation stage, Alice and Bob exchange some information to agree on the same bit string. Finally, privacy amplification technique is used to diminish the partial information eavesdropped by Eve.
System flowchart.
In the following sections, we will describe design details of each component in turn.
In this section, we describe spatial alignment which is used to transform the original acceleration data into the same coordinate system and temporal alignment which addresses the problem of synchronization.
Walking is inherently a three-dimensional movement. 3D acceleration data cannot be directly used for key generation because they are recorded at different locations and orientations. We address this problem by transforming accelerometer data of different devices to the same reference coordinate system. Figure 4 shows the definition of device coordinate system, the global coordinate system, and the body reference coordinate system. We define the world coordinate system by North, East and the Earth gravity (−G). The device coordinate system is defined as (X, Y, Z). The user plane of motion is defined as forward-sideway plane that is perpendicular to gravity. Sideway points to the right side of the body’s forward direction. Take smart watch as an example, assume the linear acceleration signals along three orthogonal directions of smart watch are Acc_x, Acc_y and Acc_z, respectively, the linear acceleration in the body reference system can be calculated by the following equation:where Acc_X’, Acc_Y’ and Acc_Z’ are linear acceleration along gravity direction, forward direction and sideway direction in the body reference system.
The different coordinate systems: the coordinate system of device is defined as (X, Y, Z). The world coordinate system is defined as (E, N, −G). The user plane of motion is the forward-sideway (F-S) plane, which is the plane perpendicular to gravity.
Temporal synchronization is necessary because different devices sample acceleration values independently. We adopt an event-based mechanism in which devices detect the time point of a heel-strike event, and use this event to trigger data collection. This method is based on the fact that the acceleration data along gravity direction reach the peak simultaneously when the foot touches the ground, and time delays in signal transmission through the body are negligible. In order to detect this event, we apply a low-pass filter on acceleration data Acc_X’ to reduce noise. The cut-off frequency is 3 Hz as we find the normal step frequency lies between 1.6–2.8 Hz [6]. Then the local peaks are detected to indicate heel-strike events as we can see from Figure 5.
The peak along gravity direction indicates a heel strike on the ground.
The proposed method eliminates the requirement of explicit synchronization as heel-strike events can be detected locally at each device without communication. When Alice receives a
Our goal is to extract exactly the same cryptographic key if and only if two devices are on the same body. Figure 6 plots the acceleration of two legitimate devices and an adversary device in three directions. Due to the fact that acceleration values of two devices on the same body are not identical as shown in Figure 6, the key generation algorithm must have the ability to map even similar sequences to the same key and less similarity to different keys. The key generation method is applied on two legitimate devices separately.
Acceleration along three directions.
After signal alignment, we obtain acceleration values along three directions: Acc_X’, Acc_Y’ and Acc_Z’. To generate keys, we perform low-pass filtering and quantization for the acceleration values in these three directions independently. As the first step, we use a low-pass filter to filter out environmental noise. The cutoff frequency is chosen as 10 Hz as the useful frequency of human motion lies below 10 Hz. After filtering, the acceleration values are normalized to have zero mean and unit length to eliminate the impact of different positions. Bits are obtained by applying the bit generation approach in [7, 8]. In the proposed system, we segment the acceleration sequence with a moving window with no overlap (window size
where
Quantization process.
After the above steps, we obtain three separate bit strings K_G, K_F, K_S. We concatenate these three bit strings together to form the initial key for Alice K_Alice = [K_G, K_F,K_S]. On Bob’s side, he will perform the same quantization process to get K_Bob.
After key generation, we often get K_Alice≈K_Bob in practice because of noise. In this section, we describe reconciliation process which is used to correct the mismatches between Alice and Bob. The main reason causing mismatches is that the samples discarded during quantization at Alice’s side may be different from Bob’s side. In order to correct the mismatches, they are required to exchange their sequence of sample indexes during reconciliation process. In this step, only the bits corresponding to common sample indexes are reserved by Alice and Bob to obtain the common key. For example, let us assume Alice and Bob each has 10 measurements in a window. After key generation, Alice obtains “01100” and Bob yields “0110”. The length of these two keys is not required to be same because they may discard different number of acceleration samples during quantization. If we assume Alice generates bits at positions 1, 3, 5, 6 and 9, respectively. She sends L_Alice = [1, 3, 5, 6, 9] to Bob. Bob checks his own position information and finds the bits are generated at positions 1, 2, 5 and 9. He sends L_Bob = [1, 2, 5, 9] back to Alice. Then they use the bits at positions 1, 5 and 9 to generate the same key as K_Alice’ = K_Bob’ = “010.”
Because walking is a repetitive activity, different steps may have high correlation, thus decreasing the randomness of the key. This problem can be addressed by privacy amplification process. In the proposed system, we use XOR function to achieve this goal. Specifically, we interleave the bit streams from three directions in time sequence and segment the concatenated keys into small windows with no overlap. We then perform XOR operation on two consecutive windows together to obtain the final key which are denoted by K_Alice” and K_Bob”.
Another function of privacy amplification is that it diminishes the partial information revealed to Eve. Because in the reconciliation stage, Alice and Bob exchange messages over an authenticated public channel and the publicly exchanged messages reveal a certain amount of information about the bit strings to Eve. Note that other privacy amplification methods such as universal hash [9] can be employed to further enhance the randomness of the concatenated key. We refer the reader to [9] for more details.
In this section, we present the evaluation results. The aims of the evaluation are twofold: (1) to evaluate the impact and performance of different parameters such as the window size (
We collected a set of accelerometer data from 20 subjects (14 males and 6 females) wearing smart devices on different locations of their body. As can be seen from Figure 8, the body positions involved in the data collection include head, chest, waist and wrist. These positions represent the common locations of mobile devices such as smartwatch and medical sensors (e.g. pacemaker). During the data acquisition, the sampling rate of all smart wearable devices is 100 Hz.
Data acquisition.
During data acquisition, the participants were asked to wear mobile devices as shown in Figure 8 and walk for about 5 minutes in their normal speed (0.7–1.1 m/s). We collect data from both indoor and outdoor environments to capture different terrains in real-world scenarios. It is worth mentioning that we do not consider the influence of different days or different walking speeds (slow, normal and fast) because all the devices worn by the subject are measuring the same gait signal simultaneously, which is different from the data collection requirement in the study of gait recognition [10]. The detected peaks that indicate heel strikes are used to synchronize acceleration samples recorded on different wearable devices and segment steps. For each device attached on one subject, we segment the acceleration sequences into small windows based on heel-strike points. In this experiment, each window contains 10 steps. Then these windows are passed to the system to generate final keys and evaluate the following metrics.
Three metrics are selected to quantitatively evaluate the performance:
To evaluate the influence of different parameters on the system performance, we first conduct a systematic exhaustive search to find the optimal combination. We vary the respective parameters within a dedicated range, i.e.
As mentioned above, each subject has acceleration samples recorded from five different body parts. For each body part of one subject, we compute the bit agreement rate of legitimate devices by using keys generated from other locations on the same body. We show the results of the average values and 95% confidence level of the performance metrics (bit agreement rate and bit rate).
In this experiment, we evaluate the influence of different sampling rate by downsampling
Influence of sampling rate.
The parameter
Impact of
In addition to sampling rate and
As described in Section 3.2.2, reconciliation process is necessary because it is used to reduce the mismatches between two parties. Figure 11 shows the influence of reconciliation process on the bit rate and agreement rate. We can see a significant improvement in bit agreement rate after employing reconciliation approach. However, we also notice that the drawback of reconciliation approach used in our system is that it will reduce bit rate. Recall that the main goal of our system is to generate the same key; therefore, reconciliation is a necessary part of the proposed key extraction system.
Influence of reconciliation.
As mentioned in Section 3.2.3, privacy amplification is used to improve the randomness of the final key. In this experiment, we investigate if the XOR function in privacy amplification achieves this goal. Figure 12 compares the entropy of the final key before and after privacy amplification. We find that the distribution of entropy is closer to 1 after XOR operation. We also notice that the entropy of the final keys varies from 0.93 to 1 which suggests that the proposed key generation protocol can generate secret keys with good entropy. It is worth mentioning that one disadvantage of using XOR function is that the bit rate will be reduced by a factor of 2 because we XOR two consecutive windows together. From the results in Figure 11, we find that the final bit rate can still reach 26 bit/sec after reconciliation and privacy amplification process. State-of-the-art cryptographic algorithms such as AES require a key length with at least 128 bits; therefore, our method takes about 5 s to generate a secure key (about 10 steps).
Impact of privacy amplification.
The generated keys will be used for encryption and decryption; therefore, we need to guarantee that the generated keys are random. In this experiment, we apply the NIST suite of statistical tests to evaluate the randomness of the final keys generated from our dataset. The final results of NIST statistical test are
NIST test | p-values |
---|---|
Frequency | 0.606072 |
FFT test | 0.562699 |
Longest run | 0.027173 |
Linear complexity | 0.386887 |
Block frequency | 0.984496 |
Cumulative sums | 0.974180 |
Approximate entropy | 0.995898 |
Nonoverlapping template | 0.302941 |
NIST test results.
In order to demonstrate the feasibility of the proposed key generation protocol on mobile devices, we have prototyped the proposed scheme on Motorola Moto E2, a state-of-the-art mobile phone. The complete key generation scheme is implemented in Java. The sampling rate of accelerometer is set as 30 Hz, and Bluetooth low energy (BLE) functionality is employed for wireless communication. BLE is designed to provide significantly lower power consumption for devices with low power requirements, such as pacemaker. Therefore, BLE is well suited for the proposed key generation protocol. Moreover, devices working under BLE broadcasting mode do not need to be paired together beforehand.
BLE is designed to provide significantly lower power consumption for devices with low power requirements. It introduces a new feature called peripheral mode, in which the data source can advertise and publish data without requiring to pair with the data requestor beforehand. BLE peripheral mode is designed for devices with resource-constraint and need to publish new data frequently. Therefore, we run the system in peripheral mode and advertise the data using broadcast packets. Bob organizes its data using Generic Attribute Profile (GATT) and encrypts the data to publish by AES. All the devices nearby including adversaries can receive the broadcast advertisements and read the public-available data from Bob. However, only Alice on the same body can generate the same key for data decryption. In this way, the private data is protected from reading by unauthorized devices.
We implement our protocol on two Moto E2 smartphones and use one of them to simulate pacemaker (Bob), the other one is used as smartphone (Alice) to request data from pacemaker. Both Alice and Bob work under BLE broadcasting mode. The main interface of Alice has a UI button that can be pressed by a user to trigger pairing process. After a user presses the start button on Alice, Alice and Bob follow the steps presented in Figure 3 to generate a shared secret key. If the length of the final key is greater than 128, only the first 128 bits are considered. After obtaining a 128-bit length cryptographic key, Bob encrypts the heart rate measurement (simulated measurement) by Advanced Encryption Standard (AES) and broadcast the encrypted message. Although several nearby devices including adversaries can listen to the broadcast packets, only Alice can generate the same key and decrypt the messages.
Table 2 presents the system overhead (computation time and energy consumption) of our system on Moto E2. The computation time and energy consumption are computed by averaging the results from 50 trials. The computation time is obtained from the console of Android studio. The energy consumption is profiled by
Computation time (ms) | Energy consumption(mJ) | |
---|---|---|
Signal processing | 108.3 | 72.7 |
Key generation | 208.1 | 12.7 |
AES encryption | 0.2 | 0.1 |
AES decryption | 0.2 | 0.1 |
Total | 316.8 | 85.6 |
System overhead measured on Moto E2 smartphone.
In this chapter, we propose and implement a key generation protocol that exploits the acceleration data produced by walking to establish a common cryptographic key between two wearable devices. Our protocol obtains a security advantage from the fact that different people have distinctive walking styles. We performed extensive experiments to evaluate the performance of our proposed system and demonstrate that the proposed key generation protocol is able to establish a 128-bit length key in about 5 s (about 10 steps). We also prototyped the proposed scheme on Motorola E2 smartphone to demonstrate the feasibility of our system on mobile devices.
There has been a global trend for populations to increasingly hold governments accountable to open government data (OGD) standards [1]. Because of this, governments have undertaken open data projects, such as providing public access to government data through publicly-accessible dashboards [2, 3]. However, government actors also may have an incentive to hide or obscure data, so there are barriers to accessing data for public dashboards [1]. This chapter focuses on the specific problem where governments attempt to demonstrate compliance with OGD standards through the presentation of a public dashboard, while at the same time, appearing to hide or obscure the data it is supposed to represent through poor dashboard design.
Our motivation to tackle this topic comes from our own disappointing experience trying to use a public dashboard implemented as part of OGD standards established where we live, in Massachusetts in the United States (US). Currently, in general, no standard guidance or recommendations are in place as a process to follow for the development of OGD public dashboards, and no framework or rubric has been proposed to evaluate them. These challenges are barriers to assessing how well public dashboards meet public need, and holding governments accountable for this. The significance of our contribution is that we propose a framework and rubric on which to base the evaluation of how well these public dashboards meet public need. The implication is that the application of this framework and rubric can be further researched in terms of utility in evaluating public dashboards. From this starting point, globally, we can begin to develop scientific consensus on what attributes in evaluate to a good-faith public dashboard implementation, and what the public should rightfully expect from the implementation of an OGD public dashboard.
The COVID-19 pandemic brought to attention a longstanding need for well-designed dashboards in public health and medicine [4]. It also brought to light that there are no uniform guiding principles behind developing publicly-facing dashboards intended to serve public interests. As a prime example, a recent review of United States (US) government public dashboards for COVID-19 found that “states engaged in dashboard practices that generally aligned with many of the goals set forth by the Centers for Disease Control and Prevention, Essential Public Health Services” (from abstract) [4]. However, the results of this review do not address whether the public was adequately served by any of these dashboards that were funded with the public’s money. Important questions not answered were: Did these dashboards meet the public’s information needs? Did they meet the information needs of public health practitioners? Or more importantly – whose information needs were these dashboards supposed to meet, and what were these needs?
At present, there is no overarching philosophy behind public dashboard design, for public health or other topics [2]. Although individual projects will publish use-cases where they discuss their design philosophy [2, 5, 6], there has not been an overall effort by the professional informatics societies or other academic groups to assemble principles behind the design for dashboards intended to serve the public. This may be because such an effort would be daunting, and would require a relatively narrow scope. The scope should be aimed at addressing high-level requirements focused on ensuring that the public’s needs are met by whatever dashboard solution is developed, regardless of the topic.
This chapter will attempt to summarize the literature into a framework that provides a general, generic rubric by which to evaluate how well a dashboard design for the public ensures that the public’s needs are met through measuring their adherence to high-level requirements. The framework will also put forth a method by which to compare alternative dashboard solutions aimed at meeting similar public needs as to how consistent the solution is with the public’s dashboard requirements.
The framework and rubric are intended to evaluate outcomes. Logically, a design process that adequately includes the public that the dashboard is intended to serve will inevitably produce a dashboard solution that meets these outcomes. Hence, there is no need to invest public funding in bloated efforts such as the Rapid Cycle Quality Improvement (RCQI) model, which is promoted by many health departments and organizations, and is extremely paperwork intensive [7, 8]. Part of what causes the RCQI model to be so effort-intensive is that it measures process outcomes. By contrast, the evaluation framework for public dashboards recommended in this chapter is streamlined, and focused on achieving a design solution, not a process solution.
Nevertheless, an optimal design solution will not be achieved without an adequate design process. Therefore, it is important to consider how the public should be involved in the process of designing public dashboards – especially those that are publicly-funded, and therefore have obligations to respond to the public’s needs.
As stated previously, there is currently no agreed-upon best-practices design process for dashboards in general, and public dashboards specifically [2]. Each time a dashboard is developed, a different design process is used. But a generic, logical process can be summarized in Figure 1.
Generic logical dashboard design process. This design process produces an alpha prototype for initial testing, and a beta prototype for widespread field testing.
As shown in Figure 1, typically, before the dashboard is designed, some sort of design process is chosen, and this design process is followed to develop an “alpha prototype”. The alpha prototype represents a working mock-up that exists for the purposes of getting feedback and working out an initial design. Next, the alpha prototype undergoes a testing process to inform developers as to modifications that are necessary before widespread testing is done. Once those modifications are made, a beta prototype exists, and can be launched for field testing.
As described in Figure 1, depending upon the project, there can be different components included in the design process for the alpha prototype. First, there will be iterative design processes as part of designing the alpha prototype, as well as the development of design documentation and the actual creation of the prototype. The details behind each of these components will vary by project. Once the alpha prototype exists, the process to convert it to the beta prototype involves some sort of user testing, and some sort of evaluation for adherence to standards. Granted, an alpha prototype may be released into the field without having undergone the beta prototype process, but that means it has not been user-tested or evaluated for adhering to standards.
This logical process can apply to any dashboard development effort. As one example, researchers aimed to design a dashboard for clinicians [9]. They wrote requirements and developed an alpha prototype, then worked with clinicians to gain feedback to guide the development of a beta prototype (which would presumably be developed in the future and field-tested) [9]. This article focused mainly on the feedback process to improving the alpha prototype, but the focus of articles can be on any part of the dashboard design process. Another article focused on the development of a beta prototype aimed at both the public and leaders for real-time decision-making related to traffic flow [2]. While the beta prototype was developed and appeared ready for testing, the article did not report any results, so the current final stage of this project was not evident in the article [2].
Although, this logical design process should theoretically involve the intended users of the dashboard, and prototypes should undergo iterative testing, this is not always the case with public dashboards. Because public dashboards often involve government agencies and leaders at some level, whether as data sources or as intended audiences, these forces can have unintended impacts on the dashboard design and quality.
As a general trend, consumers are demanding more data transparency, and calls are being made for governments to make data available for public oversight [1]. Likewise, there is an increasing trend toward using dashboards for empowering the public [2, 3]. Not only do dashboards of public data provide a mechanism for public oversight of leaders, but they also reduce
However, governments are not always keen to share the data for various reasons. It has been argued that government agencies will be more likely to comply with open government data (OGD) practices if they see it as an opportunity to showcase their agency’s success [1]. However, if the agency believes the data will cast the agency in a negative light, the agency may be less likely to be inclined toward OGD practices. Ruijer and colleagues recommend that institutional incentives and pressure be created for OGD, because governments have a natural interest in suppressing data they think may be harmful to them in some way if analyzed [1].
However, data suppression is not the only method governments employ to prevent data use and interpretation. One limitation of legal requirements for OGD is that the agency may comply with the requirements in bad faith. During the COVID-19 outbreak in early 2020, a state epidemiologist in Florida said she was fired for refusing to manually falsify data behind a state dashboard [12]. Simply reviewing the limitations of big data can reveal ways to share big data in bad faith in a dashboard, such as visualizing too much data, visualizing incomprehensible or inappropriate data, and not visualizing needed data [13].
For this reason, in addition to holding governments to OGD standards, government efforts need to be evaluated as to whether or not they meet OGD standards
The evaluation framework presented has six principles on which to judge the level of good or bad faith in a public dashboard: 1) ease of access to the underlying data, 2) the transparency of the underlying data, 3) approach to data classification, 4) utility of comparison functions, 5) utility of navigation functions, and 6) utility of metrics presented. These principles will be described below.
A dashboard is essentially a front-end, with data behind it being visualized [14]. Hence, once a dashboard is published, members of the public may want to access the underlying data for various reasons, including oversight of the dashboard. But governments resistant to data-sharing may use the dashboard in bad faith as a firewall between the public and the underlying data to prevent data access [1]. Hence, good faith OGD principles hold that public dashboards should not serve as barriers, but instead serve as facilitators to access the underlying data being visualized in the dashboard.
Although raw data are used for the dashboard, in the dashboarding process, they undergo many transformations to be properly visually displayed [9, 14]. The processing of the data can develop calculations that are then displayed in the dashboard. Therefore, to be transparent, the dashboard must not only facilitate access to the underlying raw data, but also to the transformations the data underwent in being displayed. A simple way to accomplish this kind of transparency is to use open source tools and publish the code, along with documentation [14]. This allows citizen data scientists an opportunity to review and evaluate the decisions made in the dashboard display.
How data are classified in a dashboard can greatly impact the utility of the dashboard. As an example, developers of an emergency department (ED) dashboard that was in use for five years under beta testing found that after the ED experienced an outbreak of Middle East Respiratory Syndrome (MERS), major structural changes were needed to the dashboard [15]. Another paper about developing a visualization of patient histories for clinicians described in detail how each entity being displayed on the dashboard would be classified [16]. Hence, inappropriate classifications or ones deliberately made in bad faith can negatively impact data interpretation to the point that the dashboard could be incomprehensible to its users.
Dashboards are used to inform decision-making, and therefore, being able to make needed comparisons is an important factor in a dashboard’s usability [14, 17]. As an example, the public traffic dashboard described earlier presented visualizations of the ten most congested areas of the city, as well as textual feedback on the two most suitable routes between downtown and outlying areas, to provide optimal comparators to allow the public to make the most-informed route decision [2]. While ultimately, optimal design choices could be debated, it is easy to conceive of how agencies looking to maintain opacity could obscure data interpretation in a dashboard in bad faith by deliberately limiting the ability to make useful comparisons.
Dashboards are typically at least somewhat interactive, providing the user the ability to navigate through the data display, which responds to actions by the user [14, 18, 19]. When operating in good faith, developers often conduct extensive usability testing to ensure that the dashboard is intuitive to use in terms of navigating through the data display, and that any interactivity is useful [15]. But when implemented in bad faith, a dashboard could be designed to deliberately confuse the user as to how to navigate and interpret the data in the dashboard.
One of the main purposes of dashboards is to present metrics that represent statistics or visualizations meant to summarize a particular state of the data [14, 15]. For example, in the traffic dashboard, the metrics presented are intended to communicate traffic congestion to the users, while the metrics presented in the clinical dashboard are intended for healthcare workers to use in clinical decision-making [2, 16]. In a good-faith effort, developers may conduct extensive user-testing to ensure that the metrics presented are communicative to dashboard users, as is often done with dashboards developed to serve workers in healthcare [9, 20]. However, in a bad faith effort, the metrics presented could be deliberately confusing to the user, and serve merely to hide ugly truths in the underlying data.
In the US, the Commonwealth of Massachusetts (MA) Department of Public Health (DPH) posts annual healthcare-associated infection (HAI) reports about MA hospitals on their web page [21]. The purpose of the reports is public data transparency by law [21]. Briefly, HAI, such as catheter-acquired urinary tract infection (CAUTI), central line-associated bloodstream infections (CLABSI), and surgical site infections (SSI), are serious issues because they are the fault of the hospital, and can lead to sepsis, which is a systemic infection that can end in death [22, 23]. Since catheterization happens in the intensive care unit (ICU) setting, typically hospitals track CAUTI and CLABSI as part of healthcare quality activities centered around ICUs [14]. By contrast, SSIs are tracked in association with specific operative procedures (e.g., colon surgery) [24].
In the US compared to other countries, rates of HAI are relatively high, likely because they are not required to be tracked at the federal level [14, 25]. Hospitals can opt into the federal voluntary tracking system called the National Health Safety Network (NHSN), but the NHSN does not have a publicly-facing dashboard, and the data are inaccurate, especially in undercounting severe events [14, 24, 26]. As HAI is a serious public health issue, there has been a call for greater data transparency, so the reports posted on the MA DPH web site represent MA’s attempt to comply with state-level mandates for OGD.
Although summary reports are available for download on the MA DPH web site, it is not possible to access hospital-level reports directly from the web site. To download hospital-level reports, the user must access a dashboard presented on the web page in a link (Figure 2).
MA HAI public dashboard landing page. Note: “A” labels a menu of tabs that can be used for navigation to view metrics on the various hospital-acquired infections (HAIs). In panel labeled “B”, tabs can be used to toggle between viewing state-level metrics and hospital-level metrics. Hospitals can be selected for display using a map labeled “C”.
As per Figure 2, once inside the dashboard, individual PDF-style reports can be found through navigation to the hospital in question, and the reports appear to present formatted output from a database. One way to navigate to the hospital record is to locate it on the map (“C” in Figure 2) and click on its icon, causing the panel “B” to display hospital-level metrics and a link to the hospital’s PDF report.
Each PDF report has a header displaying attribute data about the hospital (e.g., number of beds), followed by a series of ICU-, procedure-, and infection-level output. This mirrors the structure seen in the dashboard tabs and reports (Figure 2, “A” and “B”). For the ICUs at each hospital, the report displays a set of tables summarizing CAUTI and CLABSI rates, followed by time-series graphs. For a set of high-risk surgical procedures, SSI rates and graphs for the hospital are displayed. Medication-resistant staphylococcus aureus (MRSA) and
The underlying data come from the NHSN. This is not stated on the dashboard. Instead, there is a summary report and presentation posted alongside the dashboard on the web site, and the analyses in these files are based on NHSN data [21]. It seems that the DPH is using this NHSN data using as a back-end to the dashboard, and the dashboard is an attempt to comply with OGD laws.
Because the authors are aware of the high rates of HAI in the US, and because we both live in MA and we both are women who are cognizant that sexism in US healthcare adds additional layers of risk to women [28], we identified that we were in a state of information asymmetry. Specifically, we had the
In this section, we start by evaluating the existing MA DPH HAI dashboard against our good vs. bad faith framework. Next, we propose an alternative dashboard solution that improves the good vs. bad faith features of the implementation.
Figure 3 provides a logical entity-relationship diagram (ERD) for the data behind the dashboard.
Logical entity-relationship diagram for data behind dashboard. Note: The schema presented assumes four entities: The hospital entity (primary key [PK]: HospRowID), each intensive care unit (ICU) attached to a hospital which contains the frequency of infection and catheter days attributes to allow rate calculation (PK: ICURowID), each procedure type attached to a hospital (to support the analysis of surgical site infection [SSI], with PK: ProcRowID), and each other infection type at the hospital not tracked with ICUs (PK: LabID).
As described earlier, the landing page (Figure 2) provides a map by which users can select a hospital, causing the metrics for the hospital to appear in a panel. The user chooses which measurement to view (e.g., CAUTI) through navigation using the tabs. This suggests the dashboard is aimed at individuals with a working knowledge of MA geography who are intending on comparing and selecting hospitals least likely to cause HAI for an elective procedure (e.g., childbearing), or to establish as their top choice of the local hospital should they ever need to be admitted. This interface makes it difficult to compare HAI at different hospitals, because metrics from more than one hospital cannot be viewed at the same time. Further, metrics about different HAIs at the same hospital are on different panels, so within-hospital comparisons cannot be facilitated. There appears to be no overall metric to use by which to compare hospitals in terms of their HAI rates.
Figure 4 shows an example of the metrics reported by each hospital on the dashboard reporting panel (“B” in Figure 2). The figure also shows one of the two tables and one of the two figures displayed on the CAUTI tab for the selected hospital. In all, two tables and two figures are displayed in portrait style in panel “B” (Figure 2), and Figure 4 shows the top table and figure displayed. In the table displayed (labeled “1” in Figure 4), the metrics presented are the number of infections, predicted infections, standard infection ratios (SIRS), a confidence interval for the SIR, and an interpretation of the level. The figure (labeled “2” in Figure 4) displays a time-series graph of SIRs for the past five years. In the other table on the panel (not shown in Figure 4), ICU-level metrics are provided about catheter-days, predicted catheter-days, Standard Utilization Ratios (SURs) and their confidence interval, and an interpretation, and an analogous time-series graph of five years of SURs is presented (also not shown in Figure 4).
Dashboard metric display for each hospital. Note: To view hospital-acquired infection (HAI) rates at hospitals, a hospital is selected (
SIRs and SURs are not metrics used typically by the public to understand rates of HAI in hospitals. Risk communication about rates to the public is typically done in the format of
Ultimately, the design process and requirements behind this dashboard are not known. There is no documentation as to how this dashboard was designed, and what it is supposed to do. It appears to be an alpha prototype that was launched without a stated
We chose to redesign the dashboard into a new alpha prototype that met requirements that we, as members of the public, delineated. Consistent with the good faith principles proposed, our requirements included the following: 1) the dataset we use should be easily downloadable by anyone using the dashboard, 2) the documentation of how the dashboard was developed should be easy to access, 3) hospitals should present summary metrics rather than stratified ones, 4) different HAI metrics for the same hospital should be presented together, and 5) there needs to be a way to easily compare hospitals and choose the least risky hospital. To do this, we first obtained the data underlying the original dashboard. Next, we analyzed it to determine better metrics to present. We also selected open-source software to use to redeploy an alpha prototype of a new dashboard. Finally, we conducted informal user testing on this alpha prototype.
Scraping was done in open-source
We chose to focus our inquiry on the data from the hospital and ICU tables, as CAUTI and CLABSI are by far the most prevalent and deadly HAIs [23]. Therefore, we scoped our alpha prototype to only display data from the ICU and the hospital tables (although we make all the data we scraped available in the downloadable dataset). This limited us to basing the dashboard on hospital- and ICU-level metrics only.
Next, we intended to present CAUTI and CLABSI frequencies and rates, whereby the numerator would be the number of infections, and the denominator would be the “number of patients catheterized”. We felt that the dashboard’s use of catheter-days as the rate denominator was confusing to the public, and appeared to attenuate the prevalence of patients having experienced a CAUTI or CLABSI. Although, “number of patients catheterized” was not available in the data, “annual admissions” was. Since the proportion of patients admitted annually who are catheterized probably does not vary much from hospital to hospital, we chose to use the number of admissions as the denominator and a proxy measurement.
Third, we needed to develop a way of sorting hospitals as to their likelihood of causing an HAI to allow easy comparisons by public users, so we decided to develop an equation to predict the likelihood of an HAI at the hospital. We did this by developing a linear regression model with hospital-level attributes as independent variables (IVs), and CAUTI rate in 2019 as the dependent variable (DV). We chose CAUTI over CLABSI after observing the two rates were highly correlated, and CAUTI was more prevalent.
Table 1 describes the candidate IVs for the linear regression model. The table also includes the source of external data that were added to the hospital data. We studied our IVs, and found serious collinearity among several variables, so we used principal component analysis (PCA) to help us make informed choices about parsimony [37]. The data predominantly loaded on three factors (not shown). The first factor included all the size and utilization variables for the hospital; these were summed into a Factor 1 score. The second-factor loadings included the proportion of those aged 65 and older and the non-urban flag (Table 1), so those were summed as Factor 2. Proportion non-White was strongly inversely correlated with Factor 2, so it was kept for the model, and county population did not load, so it was removed from the analysis. Factor 3 loadings included teaching status, for-profit status, and Medicare Performance Score (MPS). Rather than create a score, we simply chose to include the variable from Factor 3 that led to the best model fit to represent the factor, which was MPS. Then we finalized our linear regression model, and developed a predicted CAUTI rate (
Variable | Source | Role | Source |
---|---|---|---|
Teaching hospital status | Original dashboard | Exposure | Scraped data from dashboard |
Hospital profit status | Original dashboard | Confounder | Scraped data from dashboard |
Measurements of hospital size (number of beds) | Original dashboard | Confounder | Scraped data from dashboard |
Measurements of hospital utilization (number of admissions, number of patient days) | Original dashboard | Confounder | Scraped data from dashboard |
Medicare Performance Score | Medicare performance score dataset [34] | Confounder | Medicare performance score dataset |
Non-urban county | Rural health information hub [35], United States census [36] | Confounder | Scraped data from dashboard to determine county, then application of hospital rurality flag developed from public data based on county |
Hospital county population size | United States census [36] | Confounder | Census measurements |
Hospital county population proportion below the poverty level | United States Census [36] | Confounder | Census measurements |
Hospital county population proportion below the poverty level | United States Census [36] | Confounder | Census measurements |
Hospital county population proportion of non-White residents | United States Census [36] | Confounder | Census measurements |
Numerators for rates – infection frequencies | Original dashboard | Create outcome | Scraped data from dashboard |
Infection rates | Original dashboard | Outcome | Calculated as the numerator for rates divided by admissions |
Conceptual model specification.
Next, we used the regression equation to calculate
Once the lethality score was calculated, we chose to sort the hospitals by score, and divide them into four categories: least probable (color-coded green), somewhat probable (color-coded yellow), more probable (color-coded red), and most probable (color-coded dark gray). Due to missing CAUTI information and many hospitals having zero CAUTI cases, our data were severely skewed left, so making quartiles of the lethality score to divide the hospitals into four categories was not meaningful. To compensate, we sorted the data by lethality score and placed the first 23 hospitals (32%), which included all the hospitals with zero cases, in the least probable category. We placed the next 16 (23%) in somewhat probable, the next 16 (23%) in more probable, and the final 16 (23%) are most probable. We chose to use this classification in data display on the dashboard to allow for easy comparison between hospitals of risk of a patient contracting HAI.
R is an open-source analytics software that allows for user-developed “packages” to be added
The package
The dashboard we developed was deployed on a server (https://natasha-dukach.shinyapps.io/healthcare-associated-infections-reports/) and code for the dashboard was published (https://github.com/NatashaDukach/HAI-MA-2019). When accessing the link to the dashboard, the user initially sees a map with icons (in the form of dots) on it indicating hospitals. The icons are color-coded according to the lethality score described previously. Clicking on an icon will expand a bubble reporting information about the hospital (Figure 5).
Alternative dashboard solution. Note: In our new version, two tabs are created (see “A”). The figure shows the first tab titled “ICU Rate Explorer”. The second tab, titled “data collection”, has information about the design of the dashboard and links to the original code. Each of the hospitals is indicated on the map by a color-coded icon that can be clicked on to display a bubble. The legend by “B” displays our color-coding scheme. When clicking on a hospital icon, hospital-level metrics are shown in a bubble, and there is a link that leads to the display of intensive care unit (ICU)-level metrics (see “C”).
As shown in Figure 5, like the original dashboard, this one has a map for navigation. Unlike the original, it only has two tabs: “ICU Rate Explorer” (the one shown in Figure 5), and “Data Collection”, which provides documentation and links to original data and code (see “A” in Figure 5). The hospital icons are placed on the map and coded according to our color scheme (see legend in Figure 5 by “B”). This allows for easy comparison between hospitals. When clicking on an icon for a hospital, a bubble appears that contains the following hospital metrics: Number of admissions, number of ICU beds, overall CAUTI rate, and overall CLABSI rate. There is also a link on the bubble where the user can click to open a new box that provides CAUTI and CLABSI rates stratified by ICU. Future development plans include adding other overall rates (e.g., for SSI), and adding in data from previous years to allow for the evaluation of trends.
Informally, members of two potential user bases were queried as to their reactions to the differences between the two dashboards: members of the academic public health space, and members of the MA public. When the dashboard redesign was pitched as a project to public health academics, it was dismissed as an unimportant escapade for various reasons. Some reasons cited were lack of agreement on terminology and patient safety priorities, the challenges with undercount of HAIs in NHSN data, and differential reporting accuracy in teaching vs. non-teaching hospitals. Academics also acknowledged that the system for tracking, addressing, and preventing HAIs is hopelessly broken in the US, and therefore it seems a waste of time to prop up such a system when it produces inaccurate data.
A few members of the MA public who are familiar with technology also provided informal feedback about the utility of the dashboard from a patient standpoint. They reported that the alternative solution was more intuitive than the original, and did a better job of representing the highly limited data from the NHSN.
These differences in reactions underscore the challenge of OGD and ensuring that public dashboards are developed and deployed in good faith. Those from the public health field expressed that since the system is broken and the data are inaccurate, they should be dismissed, while those in the public felt that since the data existed, it should be accessible, even if it was not completely accurate. It not only highlights the differing perspectives of those on either side of information asymmetry, it glaringly illustrates how those who are being held accountable by the usage of the data see dashboarding differently than those who are using the data to do oversight and accountability.
We wanted to compare the original HAI dashboard with the one we developed based on the good faith principles described earlier. We started by creating the framework presented in Table 2, which guides as to the good faith and bad faith characteristics of public dashboards.
Dashboard function/characteristic | Good faith | Bad faith |
---|---|---|
Access to underlying data |
|
|
Transparency of underlying data |
|
|
Data classification | Data are classified in ways that are intuitive to the consumer, and results are presented according to those classifications |
|
Comparison functions | Dashboard allows for comparisons that provide useful consumer decision-support | Dashboard prevents comparisons that would provide consumer decision-support, or makes them very difficult to make using the dashboard functions |
Navigation functions |
|
|
Metrics presented |
|
|
Proposed framework for evaluation of good faith and bad faith public dashboards.
Using this framework, we applied a rating system. We chose zero to represent “neither bad faith nor good faith”, −5 to represent “mostly bad faith” and + 5 to represent “mostly good faith”. Then, based on our experience and available information, we rated the original MA HAI dashboard and our alternative dashboard solution to compare the ratings. To experiment with applying our framework to another public dashboard, we used the information published in the article described earlier to rate the traffic dashboard which was in Rio de Janeiro [2]. Our ratings appear in Table 3.
Dashboard function/characteristic | MA Hosp | Alt. MA Hosp | Comment: MA Hosp vs. Alt. MA Hosp | Rio | Comment: MA Hosp vs. Rio |
---|---|---|---|---|---|
ACCESS TO UNDERLYING DATA | −5 | 5 | The original solution had no access to underlying data (except by way of PDF-style reports). Alternative solution posts data publicly for download. | 5 | Rio dashboard uses open data from City Hall with user-generated content collected through Waze |
Transparency of Underlying Data | −5 | 3 | It was difficult to identify the source of the data in the original solution. The alternative solution uses the same data, which is from NHSN. Because NHSN itself is somewhat opaque, the final solution lacks transparency. | 0 | Unclear from the article, but it appears that it is possible to audit Rio dashboard design if a member of a certain role (e.g., public data scientist). Not all tools used were open source. |
Data Classification | 0 | 3 | In informal user testing, public users found the data classifications much more intuitive in the alternative compared to the original solution. However, formal user testing was not conducted. | 5 | Much effort was made to classify data in Rio dashboard to make it useful for the public to make route decisions. |
Comparison Functions | −5 | 5 | In informal user testing, users found the comparison function in the alternative solution useful for decision-making, and could not find a comparison function in the original solution. | 5 | Rio dashboard was designed to allow the public to make comparisons about potential traffic routes. |
Navigation Functions | 0 | 5 | In informal user testing, users reported being able to easily navigate the data and dashboard in the alternative solution, but having extreme difficulty in navigating the original solution. | 5 | Rio dashboard for the public had a very simple, intuitive interface with images and only a few metrics critical to decision-making. This made it possible to easily navigate the dashboard display. |
Metrics Presented | −5 | 5 | In informal testing, users indicated that they did not understand the metrics presented on the original solution but found the color-coding of the alternative solution intuitive for decision-making. | 3 | The few metrics presented on the Rio dashboard were geared specifically to helping the public make route decisions based on traffic metrics. However, no formal user testing is presented. |
Total | −20 | 26 | 23 |
Application of rating system.
Note: MA Hosp = original hospital-acquired infection (HAI) dashboard from the Commonwealth of Massachusetts (MA), MA Hosp Alt. = alternative solution, Rio = Rio traffic dashboard [2], and NHSN = National Healthcare Safety Network.
As shown in Table 3, using Table 2 as a rubric and our rating scale, we were able to rate each dashboard and assign a score. We were also able to define in the comments in the table the evidence on which we based our score. Table 3 demonstrates that this framework can be used to compare two different alternatives of a public dashboard displaying the same data, as well as two completely different public dashboards. The total scores show that while our redesigned prototype of the HAI dashboard had a similar level of good faith implementation compared to the Rio traffic dashboard (scores 26 vs. 23, respectively), the original HAI dashboard had a very low level of good faith implementation compared to the other two (score − 20).
As is consistent with the global trend, the state of MA implemented an OGD requirement to share HAI data with the public through posting a public dashboard on a web page. However, as residents of MA, the authors found that this dashboard did not serve our information needs, and essentially obscured the data it was supposed to present. To address this challenge, we not only redesigned the dashboard into a new prototype, but we also tested our proposed framework for evaluating the level of good faith in public dashboards by applying it. Using our proposed framework and rubric, we evaluated the original HAI dashboard, our redesigned prototype, and a public dashboard on another topic presented in the scientific literature on the level of good faith implementation. Through this exercise, we demonstrated that the proposed framework is reasonable to use when evaluating the level of good faith in a public dashboard.
The next step in the pursuit of holding governments accountable for meeting OGD standards in public dashboards is to improve upon this framework and rubric through rigorous research. As part of this research, entire groups of individuals could be asked to score dashboards on each of these characteristics, and the results could easily be summarized to allow an evidence-based comparison between dashboards. Results can be easily visualized in a dumbbell plot (using packages
Example of visualization of framework score comparison. Note: MA Hosp = original hospital-acquired infection (HAI) dashboard from the Commonwealth of Massachusetts (MA), MA Hosp Alt. = alternative solution, and Rio = Rio traffic dashboard [
As visualized in Figure 5 and summed in Table 3, our scoring system suggested that the alternative HAI dashboard we developed was done in a level of good faith (score = 26) similar to that of the Rio traffic dashboard (score = 23), and that the original HAI dashboard appears to not have been done in good faith (score = −20), and may serve as a governmental attempt to hide or obscure uncomfortable data. This exercise shows that the framework and rubric developed can be used to compare the level of good faith in public dashboards, and to provide evidence-based recommendations on how governments can improve them so they meet both the spirit and the letter of OGD requirements.
In conclusion, in this chapter, we describe the challenge of holding governments accountable for developing public dashboards to meet OGD requirements in a way that also serves the public’s information needs. To address this challenge, we propose a framework of six principles of good faith OGD by which public dashboards could be evaluated to ensure data shared by the government under OGD policies and laws are done so in good faith. We also demonstrate applying this framework to the use-case of a public dashboard intended for residents of MA in the US to use to compare and select hospitals based on their HAI rates. As a demonstration, we present our redesign of the dashboard, then use a rubric based on the framework to score and compare the original dashboard and our alternative in terms of levels of good faith OGD. We also demonstrate using the rubric on a published use-case in the literature. As our framework and rubric provide a reasonable starting point as a method for evaluating and comparing the level of good faith in public dashboards, we strongly recommend that future research into this topic consider our framework and rubric, and build upon it through gathering evidence in the field.
As an Open Access publisher, IntechOpen is dedicated to maintaining the highest ethical standards and principles in publishing. In addition, IntechOpen promotes the highest standards of integrity and ethical behavior in scientific research and peer-review. To maintain these principles IntechOpen has developed basic guidelines to facilitate the avoidance of Conflicts of Interest.
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\\n\\nA Conflict of Interest is a situation in which a person's professional judgment may be influenced by a range of factors, including financial gain, material interest, or some other personal or professional interest. For IntechOpen as a publisher, it is essential that all possible Conflicts of Interest are avoided. Each contributor, whether an Author, Editor, or Reviewer, who suspects they may have a Conflict of Interest, is obliged to declare that concern in order to make the publisher and the readership aware of any potential influence on the work being undertaken.
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\\n\\nAll Reviewers are required to declare possible Conflicts of Interest at the beginning of the evaluation process. If a Reviewer feels he or she might have any material, financial or any other conflict of interest with regards to the manuscript being reviewed, he or she is required to declare such concern and, if necessary, request exclusion from any further involvement in the evaluation process. A Reviewer's potential Conflicts of Interest are declared in the review report and presented to the Academic Editor, who then assesses whether or not the declared potential or actual Conflicts of Interest had, or could be perceived to have had, any significant impact on the review itself.
\\n\\nEXAMPLES OF CONFLICTS OF INTEREST:
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\\n\\nAuthors are required to declare all potentially relevant non-financial, financial and material Conflicts of Interest that may have had an influence on their scientific work.
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\\n\\nPolicy last updated: 2016-06-09
\\n"}]'},components:[{type:"htmlEditorComponent",content:"In each instance of a possible Conflict of Interest, IntechOpen aims to disclose the situation in as transparent a way as possible in order to allow readers to judge whether a particular potential Conflict of Interest has influenced the Work of any individual Author, Editor, or Reviewer. IntechOpen takes all possible Conflicts of Interest into account during the review process and ensures maximum transparency in implementing its policies.
\n\nA Conflict of Interest is a situation in which a person's professional judgment may be influenced by a range of factors, including financial gain, material interest, or some other personal or professional interest. For IntechOpen as a publisher, it is essential that all possible Conflicts of Interest are avoided. Each contributor, whether an Author, Editor, or Reviewer, who suspects they may have a Conflict of Interest, is obliged to declare that concern in order to make the publisher and the readership aware of any potential influence on the work being undertaken.
\n\nA Conflict of Interest can be identified at different phases of the publishing process.
\n\nIntechOpen requires:
\n\nCONFLICT OF INTEREST - AUTHOR
\n\nAll Authors are obliged to declare every existing or potential Conflict of Interest, including financial or personal factors, as well as any relationship which could influence their scientific work. Authors must declare Conflicts of Interest at the time of manuscript submission, although they may exceptionally do so at any point during manuscript review. For jointly prepared manuscripts, the corresponding Author is obliged to declare potential Conflicts of Interest of any other Authors who have contributed to the manuscript.
\n\nCONFLICT OF INTEREST – ACADEMIC EDITOR
\n\nEditors can also have Conflicts of Interest. Editors are expected to maintain the highest standards of conduct, which are outlined in our Best Practice Guidelines (templates for Best Practice Guidelines). Among other obligations, it is essential that Editors make transparent declarations of any possible Conflicts of Interest that they might have.
\n\nAvoidance Measures for Academic Editors of Conflicts of Interest:
\n\nFor manuscripts submitted by the Academic Editor (or a scientific advisor), an appropriate person will be appointed to handle and evaluate the manuscript. The appointed handling Editor's identity will not be disclosed to the Author in order to maintain impartiality and anonymity of the review.
\n\nIf a manuscript is submitted by an Author who is a member of an Academic Editor's family or is personally or professionally related to the Academic Editor in any way, either as a friend, colleague, student or mentor, the work will be handled by a different Academic Editor who is not in any way connected to the Author.
\n\nCONFLICT OF INTEREST - REVIEWER
\n\nAll Reviewers are required to declare possible Conflicts of Interest at the beginning of the evaluation process. If a Reviewer feels he or she might have any material, financial or any other conflict of interest with regards to the manuscript being reviewed, he or she is required to declare such concern and, if necessary, request exclusion from any further involvement in the evaluation process. A Reviewer's potential Conflicts of Interest are declared in the review report and presented to the Academic Editor, who then assesses whether or not the declared potential or actual Conflicts of Interest had, or could be perceived to have had, any significant impact on the review itself.
\n\nEXAMPLES OF CONFLICTS OF INTEREST:
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\n\nNON-FINANCIAL
\n\nAuthors are required to declare all potentially relevant non-financial, financial and material Conflicts of Interest that may have had an influence on their scientific work.
\n\nAcademic Editors and Reviewers are required to declare any non-financial, financial and material Conflicts of Interest that could influence their fair and balanced evaluation of manuscripts. If such conflict exists with regards to a submitted manuscript, Academic Editors and Reviewers should exclude themselves from handling it.
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\n\nAuthors should declare if they are board members of an organization that could benefit financially or materially from the publication of their work.
\n\nAcademic Editors should declare if they were coauthors or they have worked on the research project with the Author who has submitted a manuscript.
\n\nAcademic Editors should declare if the Author of a submitted manuscript is affiliated with the same department, faculty, institute, or company as they are.
\n\nPolicy last updated: 2016-06-09
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