Age-standardized incidence and mortality per 100,000 person-years and DALYs lost per 100,000 people, for hemorrhagic stroke, in Chile in 1990–2010.
\\n\\n
More than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\\n\\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\\n\\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\\n\\nAdditionally, each book published by IntechOpen contains original content and research findings.
\\n\\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\\n\\n\\n\\n
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'
Simba Information has released its Open Access Book Publishing 2020 - 2024 report and has again identified IntechOpen as the world’s largest Open Access book publisher by title count.
\n\nSimba Information is a leading provider for market intelligence and forecasts in the media and publishing industry. The report, published every year, provides an overview and financial outlook for the global professional e-book publishing market.
\n\nIntechOpen, De Gruyter, and Frontiers are the largest OA book publishers by title count, with IntechOpen coming in at first place with 5,101 OA books published, a good 1,782 titles ahead of the nearest competitor.
\n\nSince the first Open Access Book Publishing report published in 2016, IntechOpen has held the top stop each year.
\n\n\n\nMore than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\n\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\n\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\n\nAdditionally, each book published by IntechOpen contains original content and research findings.
\n\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\n\n\n\n
\n'}],latestNews:[{slug:"stanford-university-identifies-top-2-scientists-over-1-000-are-intechopen-authors-and-editors-20210122",title:"Stanford University Identifies Top 2% Scientists, Over 1,000 are IntechOpen Authors and Editors"},{slug:"intechopen-authors-included-in-the-highly-cited-researchers-list-for-2020-20210121",title:"IntechOpen Authors Included in the Highly Cited Researchers List for 2020"},{slug:"intechopen-maintains-position-as-the-world-s-largest-oa-book-publisher-20201218",title:"IntechOpen Maintains Position as the World’s Largest OA Book Publisher"},{slug:"all-intechopen-books-available-on-perlego-20201215",title:"All IntechOpen Books Available on Perlego"},{slug:"oiv-awards-recognizes-intechopen-s-editors-20201127",title:"OIV Awards Recognizes IntechOpen's Editors"},{slug:"intechopen-joins-crossref-s-initiative-for-open-abstracts-i4oa-to-boost-the-discovery-of-research-20201005",title:"IntechOpen joins Crossref's Initiative for Open Abstracts (I4OA) to Boost the Discovery of Research"},{slug:"intechopen-hits-milestone-5-000-open-access-books-published-20200908",title:"IntechOpen hits milestone: 5,000 Open Access books published!"},{slug:"intechopen-books-hosted-on-the-mathworks-book-program-20200819",title:"IntechOpen Books Hosted on the MathWorks Book Program"}]},book:{item:{type:"book",id:"5450",leadTitle:null,fullTitle:"Polymerase Chain Reaction for Biomedical Applications",title:"Polymerase Chain Reaction for Biomedical Applications",subtitle:null,reviewType:"peer-reviewed",abstract:"Do you want to know the details that should be taken into consideration in order to have accurate conventional and real-time PCR results? 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\r\n\tTerahertz Technology reviews the developments of 0.1–3 terahertz (THz) electromagnetic (EM) waves in novel radiation–detection, fundamentals of THz optics, and the applications. The detection and radiation of THz EM waves are normally available from commercial instruments based on the mature techniques of high-peak-power lasers, semiconductors, and optomechanical fabrication. However, further applications for steering THz waves are still restricted from the methodologies of the THz propagation media, emitters and detectors.
\r\n\r\n\tIn this book, such THz technology is regarded as state of the art and is presented for the abilities to conquer technical limitations or improve performance in various applications, such as imaging, sensing, and communication systems. THz propagation media are different from those media in the infrared-ray or visible-light spectra because their specifications are designed according to the coherent lengths, photon energy, absorption, and dispersion of THz waves. Optic interference perturbs THz wave power observation in the free space because continuous THz waves have coherent lengths of 0.1–3 mm, approximating the sample thicknesses, aperture sizes, vibration amplitudes, and location deviation of the detector position. The technology of THz optics specifically presents that THz propagation materials are reliable for steering THz EM waves after the readers learn the required methodologies from this text.
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D. degree in the Department of Photonics from National Cheng Kung University (NCKU) in 2012, respectively. He worked as a physics teacher in one senior high school in New Taipei City, Taiwan in 2003, a laser application–integration engineer in HC Photonics Ltd., Hsinchu, Taiwan in 2004–2008, and a postdoctoral researcher in National Taiwan University and NCKU in 2012–2014 and 2014–2015, respectively. In 2015, he joined the Faculty of Pure and Applied Sciences, Department of Applied Physics, University of Tsukuba, Japan. Currently, he is an assistant professor to develop advanced terahertz optics based on the core technologies of fiber-sensing, artificial-material, and system-on-a-chip. He is also a member of the Japan Society of Applied Physics from 2015 to date.",institutionString:"University of Tsukuba",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"University of Tsukuba",institutionURL:null,country:{name:"Japan"}}}],coeditorOne:{id:"189825",title:"Dr.",name:"Ja-Yu",middleName:null,surname:"Lu",slug:"ja-yu-lu",fullName:"Ja-Yu Lu",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSBULQA4/Profile_Picture_1587972870854",biography:"Ja-Yu Lu received the B.S. and M.S. degrees in Physics from National Cheng Kung University (NCKU), Tainan, Taiwan in 1998 and 2000, respectively, and a Ph.D. degree in the Graduate Institute of Photonics and Optoelectronics from National Taiwan University, Taipei, Taiwan in 2007. In 2008, she joined the department of photonics, NCKU, and is currently directing Terahertz Optics Laboratory as a professor. 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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"878",title:"Phytochemicals",subtitle:"A Global Perspective of Their Role in Nutrition and Health",isOpenForSubmission:!1,hash:"ec77671f63975ef2d16192897deb6835",slug:"phytochemicals-a-global-perspective-of-their-role-in-nutrition-and-health",bookSignature:"Venketeshwer Rao",coverURL:"https://cdn.intechopen.com/books/images_new/878.jpg",editedByType:"Edited by",editors:[{id:"82663",title:"Dr.",name:"Venketeshwer",surname:"Rao",slug:"venketeshwer-rao",fullName:"Venketeshwer Rao"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"4816",title:"Face Recognition",subtitle:null,isOpenForSubmission:!1,hash:"146063b5359146b7718ea86bad47c8eb",slug:"face_recognition",bookSignature:"Kresimir Delac and Mislav Grgic",coverURL:"https://cdn.intechopen.com/books/images_new/4816.jpg",editedByType:"Edited by",editors:[{id:"528",title:"Dr.",name:"Kresimir",surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"67145",title:"Epidemiology and Management of Intracerebral Hemorrhage in Chile",doi:"10.5772/intechopen.86312",slug:"epidemiology-and-management-of-intracerebral-hemorrhage-in-chile",body:'Stroke is the leading cause of disability and the second cause of death worldwide [1]. More than two thirds of the global burden of stroke occurs in developing countries, where the average age of patients is 15 years younger than in developed countries [2]. In the period 2000–2008, the total incidence rates in low- and middle-income countries exceeded the level of stroke incidence in high-income countries by 20% for the first time [3]. Latin America is experiencing an epidemiological transition toward older urban-dwelling adults that has led to a rise in cardiovascular risk factors and an increase in morbidity and mortality rates related to both stroke and myocardial infarction [4, 5]. According to the Global Burden of Disease 2013 Study (GBD 2013), stroke is the second cause of death in Latin America [6].
Stroke is also a serious public health problem in Chile. It is the leading cause of death in Chile, with a rate of 50.6 deaths per 100,000 inhabitants in 2011 [7]. Stroke accounted for 9% of all deaths in 2010 (8888 people) [8]. In addition, stroke is the first specific cause of disability-adjusted life years (DALY) in people older than 74 years and the fifth in those between 60 and 74 years [7]. 26,072 were hospitalized with the diagnosis of stroke in Chile in 2009 [8].
The prevalence of stroke in Chile, according to the National Health Survey (NHS) 2016–2017, is 2.6% in the general population and rises to 8.2% in ≥65 years [9]. A slight increase was observed when comparing the prevalence estimated in the 2009–2010 NHS, with 2.2 and 8.1%, respectively [10].
Intracerebral hemorrhage (ICH) is the second cause of stroke in Chile and represents approximately 23% of all strokes [11]. According to the Global Burden of Disease 2010 Study, the incidence of ICH in Chile is 46.9 per 100,000 person-years; the mortality is 22.36 per 100,000 person-years, and DALYs lost are 443.9 [12]. The comparison of the incidence, mortality, and DALYs between 1990 and 2010 is shown in Table 1.
Year | Incidence | Mortality | DALYsa |
---|---|---|---|
1990 | 58.26 (42.38–76·24) | 43.21 (38.58–48.77) | 884.19 (787.41–996.20) |
2010 | 46.93 (35.24–61.38) | 22.36 (19.41–26.57) | 443.90 (385.72–519.42) |
Age-standardized incidence and mortality per 100,000 person-years and DALYs lost per 100,000 people, for hemorrhagic stroke, in Chile in 1990–2010.
Disability-adjusted life-years.
The main source of information about the epidemiology of stroke comes from the Proyecto Investigación de Stroke en Chile: Iquique Stroke (PISCIS) Study conducted in Iquique in the north of Chile in 2000–2002 [11]. This study included 69 cases of first-ever ICH. Of these, 64 (92.7%) had spontaneous ICH. The mean age was 57.3 ± 17 years, and 62.3% of the subjects were male. The age-adjusted incidence rates were 13.8 (non-lobar) and 4.9 (lobar) per 100,000 person-years. Non-lobar ICH was more frequent in young men and lobar ICH in older women. The non-lobar-to-lobar ratio was similar to previous findings in Hispanics. Hypertension was more frequent in non-lobar ICH and in diabetes, while heavy drinking and antithrombotic use were more frequent in lobar ICH, but in none significantly. There was no association between location and prognosis [13]. In the PISCIS Study, the incidence rate per 100,000 was 27.6 for ICH. The case-fatality rate for incident ICH was 28.9 (17.7–44.8). The outcome at 6 months after the first-ever ICH was 33% of patients at mRankin 0–2, 28% at mRS 3–5, and 39% dead [11].
The INTERSTROKE (risk factors for ischemic and intracerebral hemorrhagic stroke in 22 countries) Study showed that hypertension, smoking, waist-to-hip ratio, diet, and alcohol intake were significant risk factors for ICH [14]. According to the National Health Survey (NHS) 2016–2017, 27.6% of the population in Chile has hypertension; 73.3% in the subgroup ≥65 years old; 12.3% with diabetes (30.6% in ≥65); 74.2% with overweight-obesity; 86.7% with physical inactivity; 11.7% with alcoholism; and 33.3% who smoke [9].
In relation to the in-hospital management of ICH in Chile, the percentage of patients admitted with ICH varies from 14% in a private neurological intermediate care unit to 34% of stroke cases in a public hospital in Santiago [15, 16].
The most important risk factor for ICH is age. Each advancing decade from 50 years of age is associated with a twofold increase in ICH incidence [17]. In other words, ICH is more common in the elderly (1.97 x for each 10-year increase) [18]. This is a very important issue because according to the 2017 Chilean National Census, 11.4% of the population is 65+ years old [19]. On the other hand, in the Araucanía Region, 12.6% of population is 65+ years old; this region is the second oldest after the Valparaíso Region (13.6% 65+ years old) [19].
The Araucanía Region has an area of 31,842.3 km2, which represents 4.2% of the American and insular territory [20]. The region has a 17.2% poverty by income, twice the national rate (8.6%) [21]. It also has 9.9 years average schooling (11.1% national) and 29.1% rurality that it is the second at the national level [19]. The Temuco-Padre Las Casas (PLC) conurbation has approximately 360,000 inhabitants [19].
According to the 2017 Chilean National Census, 34.0% of those surveyed in the Araucanía Region stated they belonged to an indigenous or native group, a proportion significantly higher than the 12.8% registered nationally [19]. According to the National Socioeconomic Characterization Survey (CASEN) 2015, the indigenous population in Chile has worse socioeconomic indicators than nonindigenous [22]. For example, 18.3% of the indigenous population lives in poverty by income compared to 11% of the nonindigenous population; extreme poverty by income was 6.6 vs. 3.3%, respectively. Eighty-seven percent of the indigenous population is served in the public health system compared to 76.3% of the nonindigenous population [22]. There is evidence of a higher incidence of stroke among native populations and minorities [23, 24]. However, in a recent case-control study, we found no association between Mapuche ethnicity and stroke incidence. This study only included 16 patients with ICH [25].
The Araucanía Region, along with the Valparaíso, Maule, and Bío Bío regions, has double the mortality rate by stroke compared with the rest of the regions in Chile. Most of the increased risk is due to the prevalence of poverty, diabetes, sedentary lifestyle, and overweight [26]. Furthermore, according to the 2009–2010 NHS, the Araucanía Region has the highest prevalence of high systolic blood pressure compared to the other regions [10].
On the other hand, the incidence rate of stroke, calculated as a diagnosis of hospital discharge, in the period 2001–2010 in the Araucanía Sur Health Service, was 961.3 per 100,000 inhabitants/year [27].
ICH is the fourth most frequent reason for neurological consultation in the emergency room (ER) of the Hospital Dr. Hernán Henríquez Aravena (HHHA) in Temuco, Chile, accounting for 4.5–7% of the care provided by the neurologist and 13.5–18.1% of stroke cases [28, 29].
The HHHA is located in the heart of the Temuco-PLC conurbation (360,000 inhabitants), about 670 kilometers south of Santiago de Chile. The HHHA has 730 beds, is the only hospital of high complexity in the Araucanía Region, and serves a beneficiary population of approximately 800,000 inhabitants [30]. The Araucanía Sur Health Service also has four medium-complexity hospitals (nodes) and eight low-complexity hospitals. The HHHA is also a referral center for neurological emergencies from the Araucanía Norte Health Service.
The HHHA neurology unit does not have its own service and depends on the internal medicine service. Our hospital lacks a stroke unit [30]. The hospital has two CT scanners and a MRI. There is an interventional neuroradiologist (MP) during daytime hours.
The HHHA has face-to-face neurologists 24/7 in the ER since July 2013 [29]. Patients with mild ICH (ICH score 0–1) are admitted to the internal medicine service [31]. Patients with severe ICH (ICH score 2–3) are admitted to the ICU. The ICU has 54 beds (18 with mechanical ventilation) for a population of about 800,000 inhabitants. Most patients with ICH stay a long time (24–48 h) in the ER waiting for a bed in the ICU. In these conditions it is very difficult to provide the standard care to these patients, including intensive blood pressure management and general neuroprotection. Based on the results of INTERACT2 and ATACH-2 studies, our target for systolic blood pressure in the first 48 h is less than 140 mmHg [32, 33]. Intravenous labetalol and nitroglycerin are the drugs more frequently used.
Another issue is the delay for the presentation of ICH patients. In a recent study, we estimated a median of 4 h and 45 mins (P25–P75 = 3 h 13′–14 h 16′) for arrival to the ER. Just 17.4% of patients with ICH arrived in less than 3 h. In a chi-square test, the variables associated with a presentation in under 3 h were living in Temuco-PLC (p < 0.01), urban origin (p = 0.02), arrival by own car (p = 0.032), and severity (NIHSS ≥7) (p < 0.01). In a logistic regression model, only living in Temuco-PLC and severity were statistically significant with a combined odds ratio of 5.97 (95% CI = 3.23–11.04) [34].
The objective of this chapter is to report the experience in the treatment of patients with ICH in a regional public hospital in Temuco, Chile.
We performed a descriptive study of ICH in our hospital. A convenience sample of the consultations for ICH made during shift # 1 at the ER between January 2016 and December 2018 was analyzed. All patients were evaluated and diagnosed by AS in the ER. Due to the huge number of stroke patients diagnosed in the ER of the HHHA (about 220 cases of ICH per year), it was not possible to access to the clinical data of all patients with ICH in the period. We collected clinical, biodemographic, and imaging data. The radiological data were reviewed in all cases by a neuroradiologist (MP) unblinded to the clinical data. The cases were allocated to either spontaneous ICH or secondary to vascular malformation (arteriovenous malformation, cerebral saccular aneurysm, or cavernous angioma), tumor, or anticoagulants. On the other hand, the cases were classified according to one of two possible locations: supra- or infratentorial. Supratentorial hemorrhages included lobar, basal ganglia, and thalami locations. Infratentorial hemorrhages included the brainstem or cerebellum. The volume of the hematoma was calculated using the formula ABC/2, where A is the greatest diameter of the hematoma on the slice with the largest diameter, B is the diameter of the hematoma on the axis perpendicular to A, and C is the number of axial slices in which the hematoma is visible, multiplied by the slice thickness [35].
The continuous variables were described with measures of central tendency and dispersion, mean ± standard deviation (SD), and/or medians with percentiles 25–75 (P25–P75). The STATA 14.2 software was used for the data analysis.
There were 108 consultations for ICH in the period. The average age of the patients was 66.0 years (SD = 14.1). 56.5% of the patients were male. The median NIHSS was 14 points. The median time to arrival to the ER was 4 h and 45 min. The median ICH score at admission was 1 point. Only 39.8% of patients were admitted in the ICU. The mortality at 30 days was 30.6%. This value was equivalent with in-hospital mortality. The clinical and biodemographic characteristics of the ICH patients are shown in Table 2. The radiological characteristics of patients are shown in Table 3.
Characteristics | Patients (N = 108) |
---|---|
Age (SDa) | 66.0 (14.1) |
≥65 years (%) | 55.6 |
≥80 years (%) | 17.6 |
Male sex (%) | 56.5 |
Mapuche ethnicity (%) | 27.8 |
Rurality (%) | 38.0 |
Temuco (%) | 35.2 |
NIHSSb (median, P25–P75) | 14.0 (5–20) |
Time to arrival (median, P25–P75) [min] | 4 h 45′(3 h–14 h 21′) |
Time to triage (median, P25–P75) | 8 min (5–15) |
Time to evaluation (median, P25–P75) | 34 min (17–78) |
ICHc score (median, P25–P75) | 1 (1–3) |
ICH score (%) | 0 = 23.1 1 = 32.7 2 = 15.4 3 = 14.4 4 = 12.5 5 = 1.9 |
Surgical hematoma evacuation (%) | 3.1 |
EVDd placement (%) | 2.1 |
Mortality (%) | |
30-day | 30.6 |
90-day | 32.4 |
180-day | 37.0 |
Destination (%) | Intensive care unit = 39.8 Internal medicine service = 32.4 Other hospital =24.1 Dye = 2.8 Discharge = 0.9 |
Clinical and biodemographic characteristics of patients with intracerebral hemorrhage Hospital Dr. Hernán Henríquez Aravena, Temuco, Chile, in 2016–2018.
Standard deviation.
National Institute of Health Stroke Scale.
Intracerebral hemorrhage.
External ventricular drain.
Characteristics | Patients (N = 108) |
---|---|
Location 1 | |
Supratentorial | 81.5 |
Infratentorial | 18.5 |
Location 2 | |
Basal ganglia | 32.4 |
Lobar | 25.0 |
Thalamus | 24.1 |
Cerebellum | 10.2 |
Pons | 8.3 |
Volume (cm3, SDa) | 29.1 (37.6) |
Intraventricular hemorrhage (%) | 52.6 |
Etiology (%) | Hypertension = 70.4 Amyloid angiopathy = 18.5 Other = 11.1 |
Radiological characteristics of patients with intracerebral hemorrhage Hospital Dr. Hernán Henríquez Aravena, Temuco, Chile, in 2016–2018.
Standard deviation.
In our study we found several similarities with many papers about ICH. In our series the mean age of the patients was 66 years old. The same was reported by Hemphill et al. (66 ± 15 years) but is higher than the age reported by Lavados et al. in the PISCIS Study (57.3 ± 17 years) [13, 31]. This difference could be explained because we used a convenience sample with cases that were not included consecutively. In our series we included about 1/6 of the ICH cases diagnosed in the ER of HHHA (about 220 cases/year). On the other hand, the locations found were similar to the findings of Hemphill et al.: 81.5% supratentorial and 15.5% infratentorial [31]. We also found a 70.4% of the cases due to hypertension as the presumed cause. This is similar to the Hemphill study but higher than what was reported in the PISCIS Study [13, 31]. This difference in the results in comparison with our study can be explained because in the PISCIS Study, the patients were younger.
We found a 30-day mortality of 30.6% which is lower than the mortality reported by Hemphill in 2001 (45%) and similar to the 28.9% reported in Iquique, Chile [11, 31]. We also found a 6-month mortality of 37.0% which is similar to the 39% found in the PISCIS Study [11].
Unlike what was reported by Hemphill who found an association between mortality and age over 80 years, we found an association with age 65+ years (p = 0.091) [31]. In this sense we consider useful the modification in the ICH score proposed by Hegde et al. by reducing the age criteria by 10 years to prognosticate the disease better in populations belonging to developing countries like Chile [36].
Only 39.8% of our patients were admitted in the ICU. This reality is completely contrary to the clinical recommendations in developed countries. For instance, the American guideline for ICH management states that the initial monitoring and management of ICH patients should take place in an intensive care unit or dedicated stroke unit with physician and nursing neuroscience acute care expertise (Class I; Level of Evidence B) [37]. We can suppose that many patients die because they do not receive the care that the severity of their illness requires.
When presenting our results, we must emphasize that the HHHA does not have a specific infrastructure to attend to neurological patients, that is, a stroke unit. These units have demonstrated their cost-effectiveness in decreasing mortality and disability due to stroke [38]. In our situation, not all ICH patients are admitted to the ICU and complete 24–48 h of observation in the ER, being later hospitalized at the internal medicine service.
About 80% of the population in Chile is treated in the public health system [21]. Users of the public system have worse health indicators than users of the private health system [9]. On the other hand, it is expected that the incidence of ICH will increase significantly in our country due to the aging of the population and the poor control of cerebrovascular risk factors. This is why we see the need to have a stroke unit and/or a neurologic intermediate care unit in our hospital for the adequate management of patients with ICH. We also hope to set a Telestroke system with the future primary stroke centers in our region (Nueva Imperial, Pitrufquén, Lautaro, Villarrica, Victoria, and Angol hospitals). In short, we hope that the HHHA will become a comprehensive stroke center [39]. We also consider a priority to develop a better access in the detection and treatment of all the vascular risk factors mainly the control of hypertension. In the NHS 2003 in Chile, only 60% of the hypertensive knew their condition, 33% were being treated, and only 30% had normal values [40].
ICH is a common cause of consultation in our hospital, especially in older people. The implementation of 24/7 neurology shifts in the emergency room allowed us to reduce the evaluation time and to improve the management of ICH patients; however, it is still difficult to admit ICH patients to the ICU. We are aiming for a soon implementation of a stroke unit, so ICH patients receive a standardized care. It’s a main priority to have better access to primary care prevention, diagnosis, and treatment in developing countries like ours.
This study is funded by the Universidad de La Frontera.
None.
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Game Theory is a branch of mathematics that are used to model the strategic interaction between different players on a context of predefined environment. At management game theory there is predefined organization environment where the players are leaders and team members as workers or employees. Each player has incentives that drives their behavior in the game. Management game is non-symmetric because leader has specific and non-changeable characteristic compared to workers. Workers are motivated in maintaining and improving their work performance and personal self-esteem. Team leaders are motivated in maintaining and improving team performance, which is related to team leader personal profit incentives. Team leader knows that team performance is essential for achieving team profit targets. Workers know that their personal incentives will improve if their work performance is good. Thus, if there are problems at work the rational policy would be to tell the problems to supervisor so that problems can be solved. In addition, solving problems may improve workers self-esteem, having hidden psychological incentive. This organization environment form state space for strategic-Bayesian-stochastic-nonsymmetric-signaling game.
Nash equilibrium is a concept of game theory where optimal outcome is the balance where all players incentives are considered and fulfilled in optimal way. If team leader gives positive feedback for raising the possible problems, it will have positive effect on workers’ self-esteem, fostering workers policy to inform the problems by signals. Solving the problems will improve group performance which foster leader’s policy to encourage workers signaling game. This way workers and supervisor may find equilibrium of policies (strategies) which lead to general-sum game where optimal and sustainable team profit performance is achieved. However, this article explains why this optimal equilibrium is difficult to achieve in reality. Bersin [1] study reveal that 89% of managers think that leadership is important issue, but current leadership programs bring only minor value in improving leadership quality. This article argues that modern reinforcement learning artificial intelligence gives one solution in solving leadership challenge.
In addition to administrative role, the HR management has important function on adding competitive business value to an organization management (for example see references [2, 3, 4, 5, 6]). Managers need predictive measurements that indicate how business is developing and how to improve it. Human assets are essential for creating competitive advantages, thus interest in performance management has increased. Fleetwood and Hesketh [7] argue that researchers should better understand the complexity of the organization environment and seek to open a “black box” of causal relationships between human resources and organizational performance rather than offering simplified solutions.
Several studies indicate that employee psychological well-being has tendency to predict business value of an organization (for example see references [2, 8]). However, management can be confused of how to improve well-being and how much effort should be invested in well-being development at different situations to gain sufficient payback. Research reveals that organizations expect artificial Intelligence to help reducing managerial biases related to human issues and to improve productivity and employee experience [9]. Beside the hopes, researchers are also concerned that artificial intelligence may cause serious harm if the organization context is oversimplified by using data driven machine learning algorithms [10]. This article argues that AI can help solving difficult management problems related to human biases. One of the most promising new technology is Digital Twin that uses simulation model driven AI. “To build an efficacious Digital Twin, it’s important to first agree what problem needs to be solved or what opportunity needs to be explored and how accurate do the predictions need to be” [11].
Human competencies, for example leadership and working skills, have certain causalities to long term productivity. It seems that human competence has three performance-driving characteristics that can be described according to motivation theory as feelings of safety, team culture, and passion for work. It is clear that a passion for work affects a person’s performance in a very different way than for example occupational safety issues. In addition, human is a psychophysical entity tied to his own situation. Therefore, the combination of all motivational drivers determines performance [12].
First, we have to study human capital productivity, which includes working time and the utilization of intangible human assets. Human intangible assets refer to performance on how effectively is the working time utilized, and how much value a person produces at each working hour. An employee may work for eight hours a day, but out of that working time, how much is actually used effectively in creating value? This basic understanding of how each employee produces value needs to be recognized before any reliable simulation analytics can be made.
At this article the organization digital twin (ODT) refers to the mathematical environment that simulate organization human capital productivity. To be able to simulate the reality the digital twin must meet following requirements:
Markov property: The future is independent of the past given the current situation.
The environment state can be verified from measuring the reality.
Markov property means that the future is not determined by the past data, thus supervised learning regression analytics cannot be solely applied in creating ODT. Markov rule is one backbone for creating ODT digital twin and for utilizing Reinforcement Learning where the behavior of the agents determines the future.
The state transition from state to state follows Markov chain where all necessary information is transferred from past to the present. Therefore, the probability of transition from the current state to the next state depends only on the current data and the activity of the players. In the digital twin, this current data must be able to determine the reality presented by the twin. The data in the twin can be measured and verified from reality, thus creating a feedback loop from the real world. This model verification against reality is also necessary for learning purposes so that ODT can learn to refine the transition functions to match the real world (Figure 1).
Concept of organization digital twin.
New science provided theoretical framework for creating ODT for modeling organization human capital productivity. First, there should understand how employees produce economic value. The theory of Quality of Working Life (QWL) determines the effective working time-share from the time spend at work. According the human capital production function the staff effective working time multiplied by K-coefficient produces customer value that is measured by revenue. The coefficient K describes the business branch, tangible investments and business logic. QWL improvement requires HR-development that increase auxiliary working time, thus reducing time for work [12] (Figure 2).
Illustration of profit team human capital production function.
The human capital production function can be written in function where revenue is the production volume according the Equation [13]:
and
Where
R = Revenue [$].
K = Coefficient for effective working time revenue relation, HR business ratio [$/h].
L = Labor capacity in full-time equivalent [pcs].
TWh = Theoretical yearly working time [h].
QWL = quality of working life, indicating human capital intangible asset utilization (0–100%).
Ax = The auxiliary working time of the total theoretical working time (vacation, absence, family leave, orientation, training, HR practices, and HRD) [%].
(1 – Ax) = (100% – Ax) = Time available for actual work (time spent at work)
(1 – Ax) * QWL = Effective working time from the theoretical working time.
It should be noted that other working time includes so-called internal error factors such as waiting, searching, correcting and unnecessary work. These are symptoms of different kinds of development needs that the team has noticed either hidden or conceptual.
When manager does efficient team development there can be increase in effective working time. In addition, if absence and staff turnover is high the development may reduce those, thus increase time for work. This way effective team development will increase effective working time, and have good effect on profit. However, at short notice the development will increase auxiliary working time and reduce time for work, thus will reduce both revenue and profit. The development of human capital involves the phenomenon of investment, which requires some sacrifice in order to gain delayed rewards. When investment phenomenon is involved in the agents’ actions there is possible to utilize Q-learning function.
Q-learning is a mathematical method for analyzing behavioral learning points in a simulation model that considers short- and long-term rewards. Nash equilibrium is the result where Q-learning settles to a certain level where the model environment is stable and no player can improve his pay-off [14]. In this case, equilibrium is achieved with a behavior in which both QWL and profit mature to a certain level. Both QWL and profit are management game agents’ rewards, which in short-term may be contradictory because improving QWL reduces profit in short-term. This article shows that there are several states of equilibrium in a leadership game.
In most traditional well-being and commitment surveys, scores are averages of factors that are not individually relevant to the whole. Thus, the result is for example engagement index that does not necessarily tell what and how to improve and what impact the improvement would likely have. Traditional well-being surveys with average scores are oversimplified when measuring human performance. For ODT perspective, it is essential that the staff performance is determined realistically. It affects to the rewards and transition functions of agents’ behaviors. Therefore, it is essential to describe the theory of QWL.
It seems evident that human performance is rather complex phenomenon, consisting several motivation theoretical aspects that cannot be included at simplified statistical staff survey analytics. Therefore, we have utilized motivation theories of Alderfer [15], Antonovsky [16], Kano [17] and Herzberg [18] in creating advanced human performance theory that meets the contribution of main scientists and forms practical QWL index for performance analytics. QWL index includes three self-esteem categories, which each has unique effect on performance.
The self-esteem categories:
Physical and emotional safety (PE);
Collaboration and identity (CI); and
Objectives and creativity (OC).
Chosen categories and their effect on performance form the theory of QWL index. It is also important to know that in addition that QWL index is production parameter, it has also logical connection to customer satisfaction (see [17]) (Figure 3).
The theory of QWL.
Finally, the QWL index is the combination of all three self-esteem factors according the following equation:
where
QWL is calculated using the quality of working life index (0 … 1).
PE(x1) is the value of the function of physical and emotional safety.
CI(x2) is the value of the function of collaboration and identity.
OC(x3) is the value of the function of objectives and creativity.
The functions of the self-esteem categories are adjusted so that the final QWL result is always between 0 and 100% [12].
Bayesian theorem with stochastic game is utilized in defining management-game for ODT. Using game theory, we can model the strategic interaction between different players (agents) in a predefined environment. Our management-game is multi-agent game for the profit unit where the agents are workers and manager. The concept is non-symmetric because manager (team leader) and workers have different roles and their reward characteristics differ. Workers are motivated to maintain and improve their self-esteem (QWL). In addition, there might be some hidden motivation drivers. Team leader motivation drivers are unit profit and possible personal incentives, which may be hidden (e.g. biases). In our game the focus is profit-unit manager’s behavior and learning.
At Nash equilibrium the optimal outcome of the game is one where no agent wants to deviate from the chosen policy because that seems to be parallel with opponents’ policy. Workplace problems have reducing tendencies on workers’ self-esteem, thus decreasing QWL as a production parameter. Management practices have tendencies to improve QWL, but each action will reduce short-term profit. Manager’s strategy hypothesis guides the actions at different state events. When the consequence data of action tendencies update the status after each Markov-sequence, the player can update the management strategy, which further controls the next actions. Bayesian probability is related to player subjective behavior, relying on the phenomenon that rational thinking will probably lead to optimal result as the new information comes available [19].
The manager should learn the optimal leadership strategy without knowing the exact reward function or state transition function. This approach is called stochastic model-free reinforcement learning and can be defined with the Nash Q-learning approach. The leader has prior-believe about the state of nature of profit-unit business situation and expected future reward. The uniqueness of the game comes from the fact that it has predictive features that allow for the use of reinforcing learning artificial intelligence for learning Nash equilibrium between staff QWL and organization profit.
Management game is signaling game since workers give essential signals about possible workplace problems that may threaten their self-esteem (QWL) and therefore team performance. Workers preference strategy is to give their leader signals about the problems. In simplified digital team leaders’ learning-game the worker’s strategy may be stationary, meaning that workers behavior may be chosen in advance when the events scenario is known.
Team leader, as an agent of the management game, is responsible for team profit performance that is the outcome of producing customer value measured by revenue. Agent registers workers’ signals and makes own prior belief for the strategy. Agent monitors also scorecards from business outcomes of monthly and cumulative profit, and forms a prior believe policy on how to act to these measures. Agent is rewarded by the profit at each month and cumulative profit at the end of the year. After each state transition the agent will get profit signals and QWL signals from the worker’s response from the state change at workers QWL. State-change signals and reward results may cause changes at the preference strategy of the agent for the next sequence (Markov sequence [19]) (Figure 4).
Leader’s prior believe is biased and this strategy leads to delayed punishment.
Leader reward function is (γL) the combination of monthly profit change, and expected affect to future profit. πLi is the leader’s strategy at current state (month). It seems that at the beginning, the leader strategy is weighted at the monthly profit and the expected future reward is based on simple linear regression of data achieved so far. This means biased prior believe where the expected reward is not nearly the same as the outcome of the strategy. Thus, the value function under biased strategy is the following:
where
γt€ is the observed state reward.
γ12€ is the expected future reward.
When the leader gets more experience and learns to understand the complexity of the system as well as the meaning of workers’ QWL, the prior believe value function changes. QWL change starts to be more interesting, because leader learn to expect more future profit when QWL improves. Thus, along this information the leader adjusts the strategy for optimizing cumulative yearly profit. Here the leadership game stochastic nature is key to learning the Nash general sum equilibrium between the QWL and profit.
where
γt€ is the observed state reward.
γ12€ is the expected future reward by improving the QWL.
αt is the learning rate.
QWL is improved by leadership actions that reduce the monthly working time for making the revenue. Thus, improving QWL reduces monthly revenue and profit, but may increase effective working time in the future and so increase the future profit. In monthly basis, this phenomenon may be contradictory and confusing, but by practice, the best reward is achieved where both workers’ and leader’s payoff functions flourish. This means the Nash equilibrium where yearly QWL is improved with high profit. In Nash equilibrium, leader’s choices are the best response to the workers’ signals and business cumulative outcome at the end of the year.
Bayesian stochastic strategic non-symmetric signaling learning game follows Markov decision process [20, 21, 22]. Management-game forms stochastic game tuple
where
N is set of players, i.
S is set of states, s.
C is set of competences at actions a.
A is set of actions, a.
T is set of signals, τ.
P is transition probability function; P: S x A x C thus P(s, c, a), ρ:SxA|C → Δ is the transition function, where Δ is the set of probability distributions over state space S.
R is reward function, R = r1,…rn, γ:SxA|C → R.
There is incomplete but perfect information. The agents (workers and leader) do not know other agents’ payoff functions in detail, but they can observe other agents’ immediate payoffs and actions from past months. A leader does not know exactly which actions would be the best but can choose actions that should be good enough. The leader will get workers emotional feedback immediately and information from profit monthly change and cumulative reward. After several game rounds, the player (leader) will learn the optimal actions to improve both the QWL and annual profit. Thus, the player will achieve the Nash equilibrium of stochastic Markov learning game.
Management-game has context specific Markov-sequences. State and state change transition follows the Markov property where the future is independent of the past given the current situation. Once a state is defined, its change is determined by the behavior of the parties. State change is sequential, following the players actions and state transition probability function. Sequences are:First Month (January)
Workers interpret the state situation and give signals based on prior believe (τ).
Leader observes the signals and updates the signal-strategy (πτ).
Leader updates standard-strategy (πst). Note: at this first month there is no data to update this year profit strategy.
Leader makes actions (or decide doing nothing) (a)
Actions leads to state change with possible outside intervention (stochastic)
Leader observes immediate (γ€1) and cumulative (γΣ€) profit rewards (or sacrifices). From now on, the leader gets also profit outcome, thus updates also profit strategy.
According the combination of rewards, the leader upgrades prior believes concerning own behavior
Leader upgrades profit-strategy and standard-strategy for choosing actions t + 1
Workers give signals to be considered when deciding actions t + 1
Leader updates signal-strategy for choosing actions t + 1
11–13. Leader makes actions t + 1 in line with all three strategies.
14. State transition to state t + 1
15… From now on, the supervisor should update all three strategies simultaneously as learning sequences progress (Figures 5 and 6).
Management-game Markov sequences.
Management game learning phenomenon for finding equilibrium.
Leadership game Q-learning function is (7)
where
β is [0,1] is discounted reward factor
αt is [0,1] is the learning rate (1-αt)
γ∆i is the monthly profit reward
γ∆12 is the expected cumulative profit reward (floating 12 months)
With expected equilibrium strategy pay (
There are three different strategic areas of prior-believes that forms the manager’s learning context. These strategies are influenced by the supervisor’s interaction skills (competences), which tend to either promote or hinder learning in the area. Every manager has personal competences, which seems to form personal Nash equilibrium and corresponding Q-learning results. According to this article, it seems that Nash equilibrium is different for each combination of manager’s competences. In addition, the leader’s strategic mind-set defines the equilibrium. Indeed, management equilibrium seems to be evolving phenomenon, depending on organization and its’ players change of characteristics (Figure 7).
Management learning strategies.
The focus of the signal-strategy (
Profit-strategy (
The focus of the standard-strategy (
All of these supervisor strategies are built on the supervisor’s personal and ever-evolving managerial skills. In this management game theoretical approach there are personal leadership action competencies that determine the effect of each action. There is interaction between management competencies and learning strategies. The supervisor reflects the effectiveness of his or her own leadership behavior and changes personal management strategies accordingly.
Digital twin advisor uses Bellman [20] expectation function in finding optimal actions for achieving Nash equilibrium. Bellman expectation function for strategy π is
where
Optimal policy forms from the actions that result in optimal value function, thus
where
In our digital twin AI assistant is using Bellman function. It returns the combination of actions that gives the best value after floating 12 months. This is achieved so that first each action value is analyzed and sorted in magnitude of the value. Then the combinations of best actions are evaluated until marginal productivity of the value is achieved, see example at Figure 8. One simulation episode is 12 months; thus, the Bellman function maximize future reward even when the episode is coming to end.
Bellman function principle of marginal productivity value.
Simulation game is done using UNITY 3D, for making possible to play the learning game episodes. Each episode is 12 months, consisting several workplace challenges. In the test runs we used Cash Cow episode where problems are easy, the market situation is steady, and the company does not seek special increase in revenue. State space problems are signaled by the workers that comes meeting the team-leader (agent). In this ODT there is so far 25 workplace challenges which reduce QWL according situational probability matrix. Leader has 32 best management practices (action space) that may be used as the leader prefers. Each action reduce profit and may improve QWL according state space situation specific probability function [23] (Figure 9).
Simulation game user-interface.
We tested simulation using three different competence values; 30%, 60% and 90%. Table 1 contains the results of three simulation rounds as follows:
BIAS = human simulation episode (round) with bias to maximize short term profit. Only problem-solving actions are made. In BIAS episode the focus is on maximizing short-term profit.
Learning = human simulation episode where leader has learned to maximize best result in QWL and profit. Agent execute best learning strategy (see Figure 7) with long-term profit mind-set, problems solving as good as possible and following yearly management-plan of actions.
Bellman = artificial intelligence episode where all actions are chosen according Bellman function (see Figure 8).
Q-learning | QWL start | QWL end | QWL difference | Cumulative | Profit difference | Equilibrium in 1 y. | ||
---|---|---|---|---|---|---|---|---|
Budj. € | EBITDA | |||||||
Competence 30%, BIAS | 3 310 | 60,2% | 57,9% | -2,3% | 254 923 | 244 921 | −10 002 | — |
Competence 30%. Learning | 5 370 | 60,2% | 64,6% | 4,4% | 254 923 | 243 650 | −11 273 | yes |
Competence 30%, Bellman | 21412 | 60,2% | 67,5% | 7,3% | 254 923 | 257 070 | 2 147 | yes |
Competence 60%, BIAS | 5 854 | 60,2% | 59,0% | −1,2% | 254 923 | 263 284 | 8 361 | yes |
Competence 60%, Learning | 20 425 | 60,2% | 68,3% | 8,1% | 254 923 | 287 083 | 32 160 | yes |
Competence 60%, Bellman | 35 931 | 60,2% | 70,4% | 10,2% | 254 923 | 293 442 | 38 519 | no |
Competence 90%, BIAS | 7 737 | 60,2% | 59,8% | −0,4% | 254 923 | 276 828 | 21 905 | yes |
Competence 90%, learning | 31 240 | 60,2% | 69,9% | 9,7% | 254 923 | 305 604 | 50 681 | no |
Competence 90%, Bellman | 38 446 | 60,2% | 70,1% | 9,9% | 254 923 | 312 003 | 57 080 | no |
Test episode values.
It seems that with management competence levels 30% there are difficulties to achieve budgeted target result in profit. If QWL is sacrificed for short term wins, the cumulative profit result at the end of the year will be poor. It seems that in one-year simulation episode there is achieved equilibrium where Q-learning points and QWL values are not exceeding. At 30% competence levels the BIAS episode Q-learning points varies between 0 and 3000 points. It seems as if the agent has no idea of how to achieve sustainable development where both QWL and profit improves. With low competence levels only with Bellman decisions will the profit slightly exceed the target value.
Manager’s competence levels 60% are quite realistic, representing average line-managers leadership-action skills. In one-year simulation both BIAS and Learning strategies achieve Nash equilibrium, however in different profit outcome. At BIAS strategy the QWL is set at level 60%, which actually corresponds workforce medium QWL value in Finland [24]. When equilibrium is achieved, it may be difficult to change the behavior (see Figure 10).
BIAS strategy Q-learning points.
Learning strategy has also equilibrium at competence level 60%, but higher QWL and profit values than in BIAS (see Figure 11). In our practical simulation studies this type of results are usually learned when simulation episodes are practiced over ten times. Must bear in mind that management systems have tendency to press maximizing short-term profits, thus remaining in BIAS mind set. Learning to be excellent leader requires several years practice in organization system that allows investing in people. This phenomenon may explain why some leaders learn to be excellent team-leaders while majority remains at lower level.
Learning strategy Nash Q-learning equilibrium.
There is interesting phenomenon at 90% competence level BIAS strategy. Even with very high leadership skills the QWL is set at 60% where equilibrium remains. This is due to the behavior where leadership actions are implemented only when problems arise, thus there are no proactive investments in team development. In competence levels 90% it seems that one-year simulation episode is not enough time to achieve perfect equilibrium at Learning and Bellman strategies, since Q-learning points and QWL seems to continue improving throughout the episode. It would need longer time period to achieve equilibrium.
BIAS strategy seems to achieve equilibrium where QWL is no longer improved and the Q-learning points finds management cultural maximum value. The lower the competence, the lower the level of QWL, however the difference is not so big, varying from 57% to 60%. This is interesting because in Finland the workforce medium QWL is around 60% [24]. One could argue that the profit maximization bias is common and not depending on line-managers leadership competences, and therefore most employees feel the QWL is around 60%. Moreover, the reason for profit maximization bias is not necessarily a lack of leaders’ skills, but a management system that forces leaders to focus on short-term profit rather than people.
Organizational management research has typically focused on qualitative behavioral factors that have a complex relationship to organizational success, and in addition, impacts often come with a delay. Each organization is a unique system with certain same laws, but also a unique context of its own. Therefore, repeating the empirical research results has proven to be challenging, which also makes it difficult to draw generalizable conclusions [7]. This article examines the utilization of model-based artificial intelligence in management development. ODT can be used to assess the impact of management behavior on an organization’s success, considering situational data and the impact of management culture. ODT helps to explore the fundamental nature of an organization, which means a metaphysical essence in where everything affects everything.
The article uses artificial intelligence to illustrate how leadership behavior can create a so-called QWL glass roof that invisibly prevents teams from growing to the top performing category. The management system forms the behavior of supervisors in such a way that harmful biases of management thinking may occur, in which case people’s performance does not develop favorably. These harmful biases of thought are very complex as they include phenomenon of delayed effects on an organization’s competitiveness. Model-driven reinforcement learning artificial intelligence reveals a variety of human and complex mechanisms that hinder the development of competitiveness.
Reinforcement learning is following rational learning phenomenon, where learning take place gradually, according the experience. Simulation model provides learning platform where person can learn without fear of remorse. This is essential especially for managers, because in real life there is hardly room for learning from mistakes. The ODT models the situation with the organization’s own data. The simulation can be designed according to the company’s own strategy, allowing future challenges to be practiced. This allows management and supervisors to adapt in advance and prepare for future challenges. More proactive management reduces the realization of personnel and business risks and adds value to performance. For example, adapting to a recession can be practiced, as can market growth, both of which require a different way of managing. Artificial intelligence combined with the digital twin helps to emphasize leadership skills and practices that lead to sustainable development.
ODT has been used in college students’ leadership studies. Learning outcomes have been monitored through self-assessments, and the results are encouraging. Gamified simulation-learning is based on reinforcement learning, where progress takes place through experiential adaptation according to the student’s capabilities and learning ability [25]. ODT is also used in managerial trainings for companies and municipal organizations. Perhaps the biggest challenge in coaching supervisors in working life is unlearning the biases that prevent leadership success. Traditional teaching is largely based on sharing best knowledge, where the teacher shares information on how to act and why to behave in a certain way. The power of digital simulation teaching is based on the fact that it adapts the brain through experiential learning. When a supervisor has to change the prevailing leadership attitude, he or she kind of adapts the brain to another frequency where listening and caring for employees rises higher in priorities. In this way, the supervisor becomes interested in developing herself in interaction practices where she may not have previously felt the need to learn.
The architecture of the digital twin models the reality of an organization with relatively good accuracy, which is important in building trust in an artificial intelligence solution. The core of the model is in the Human Capital Production Function of and in the scientific research of the Quality of Working Life index [26]. The architecture lays the foundation for a neural network that has been fine-tuned with the probabilities of empirical research as well as correlations created through supervised learning. For example, the physical and emotional safety (PE) of the QWL index correlates with sickness absence, so that when the PE factor falls, sick leaves increases. The correlation is brought into the digital twin, which makes the model more accurate because it also models sick leaves. In addition to research data, the digital twin can be calibrated with data from the organization. ODT learning can be extended in the organizational hierarchy to the level of an individual supervisor. In this way, artificial intelligence learns the strengths and weaknesses of a leader, so that the advice given by artificial intelligence is targeted at each supervisor.
Supervised learning AI that is based on data alone is unable to “understand” organizational complexity and phenomenon of delayed impact relationships. In fact, there is a word of warning in using simple data-driven AI in complex organization environment, because it may strengthen the harmful behavioral biases. Article indicates that ODT with Bellman algorithm can be used in finding organization specific optimal behavioral patterns and measures which will form sustainable competitiveness. The article suggests that in the future, top-tier companies will use RL artificial intelligence to support management decision-making.
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