HEALTH CARE
Author: Sheetal Nerlekar
MMS – Roll no (0222104)
Kohinoor Business School
Literature Review
Why do Family Members Reject AI in Health Care? Competing Effects of Emotions.
Park, Eun Hee1, et al (2021) says that,”Why do Family Members Reject AI in Health Care? Competing Effects of Emotions” investigates the reasons behind the rejection of AI monitoring for healthcare by family members. The research is based on two scenario-based experiments and explores the competing effects of emotions towards the rejection of AI monitoring. The study aims to understand the emotional factors that influence the acceptance or rejection of AI in healthcare.
Federated Learning for Smart Healthcare: A Survey.
DINH C. NGUYEN1, et al (2023) says that, “A Survey” investigates the use of federated learning in smart healthcare. Federated learning is a recent development in machine learning where the computation is offloaded to the source of data, which appears to be a promising solution to the problem of centralized data collection and processing that may be infeasible in realistic healthcare scenarios due to the highly sensitive nature of patient data. The research provides a comprehensive survey of applications of federated learning for healthcare informatics and discusses the potential of federated learning to produce generalizable models that will help achieve equitable, effective, and patient-centered care. The study aims to provide insights into the use of federated learning in smart healthcare and its potential to address the issue of data unavailability and get a shared model.
Inequalities in Health Care Services Utilization and the Impact of the COVID-19 Pandemic in Bulgaria.
Rohova,Maria1,et al(2022) says that, “The search results suggest that the COVID-19 pandemic has exacerbated existing inequalities in health care services utilization. The pandemic has exposed the health effects of longstanding social inequities and has put a spotlight on health equity as an essential issue. Inequalities in health during COVID-19 are exacerbated by interweaving risk factors and comorbidities that unfavorably magnify the disease. Vulnerability to disease is linked to social determinants of health, such as race, ethnicity, and socioeconomic position. The pandemic has laid bare the effects of existing inequities, particularly those related to race and socioeconomic position. Addressing health care disparities requires a multipronged strategy. “Inequalities in Health Care Services Utilization and the Impact of the COVID-19 Pandemic in Bulgaria.” The search result shows that the research is classified under the JEL classification system, but no further details are provided. It is possible that the research article was not published in any of the sources indexed by the search engine or that the search terms used were not specific enough to locate the article.
EXCEPTIONAL EFFICIENCIES: A VALUABLE DEFENSE FOR HEALTHCARE MERGERS.
Gibson, Matthew G.1,et al(2023) says that, “Exceptional Efficiencies: A Valuable Defense for Healthcare Mergers” investigates the role of exceptional efficiencies in defending healthcare mergers. Exceptional efficiencies refer to efficiencies that are not typically achieved in the ordinary course of business and are significant enough to offset any anticompetitive effects of the merger. The research aims to provide insights into the use of exceptional efficiencies as a defense for healthcare mergers and their potential impact on competition in the healthcare industry. The study suggests that exceptional efficiencies can be a valuable defense for healthcare mergers, but their use requires careful consideration of the potential anticompetitive effects of the merger and the magnitude of the efficiencies. The research provides guidance on strategic planning, analysis of the health services environment, and lessons on implementation for healthcare organizations.
Social prescribing and the search for value in health care.
Fleming, Mark D.1,et al(2023) says that, “Social Prescribing and the Search for Value in Health Care” investigates the use of social prescribing to improve the value of care, defined as the amount of health achieved per dollar spent. Social prescribing involves the use of non-medical interventions, such as community-based activities and support services, to address social determinants of health and improve health outcomes. The research aims to provide insights into the use of social prescribing as a means of improving the value of care and its potential impact on healthcare delivery. The study suggests that social prescribing can be an effective way to address social determinants of health and improve health outcomes, but its use requires careful consideration of the potential benefits and costs. The research highlights the importance of integrating social care into the delivery of healthcare and the need to tackle the inequitable distribution of power, money, and resources that drive systematic inequalities in health outcomes.
Provider opinions of the acceptability of Ask Suicide-Screening Questions (ASQ) Tool and the ASQ Brief Suicide Safety Assessment (BSSA) for universal suicide risk screening in community healthcare: Potential barriers and necessary elements for future implementation
Christensen LeCloux, Mary1, et al (2022) says that, “Provider Opinions of the Acceptability of Ask Suicide-Screening Questions (ASQ) Tool and the ASQ Brief Suicide Safety Assessment (BSSA) for Universal Suicide Risk Screening in Community Healthcare: Potential Barriers and Necessary Elements for Future Implementation” investigates the acceptability of the ASQ tool and the ASQ Brief Suicide Safety Assessment (BSSA) for universal suicide risk screening in community healthcare. The study aims to provide insights into the opinions of community healthcare providers regarding the acceptability of the ASQ tool and the BSSA and to identify potential barriers to implementation. The research found that the majority of participants were comfortable screening for suicide with the ASQ tool and the BSSA and would recommend them to colleagues. However, barriers to implementation reported included a lack of knowledge regarding suicide risk screening and lack of access to behavioral health resources. Education regarding the ASQ, the BSSA, and suicide risk screening is highlighted as crucial elements for future implementation. The ASQ tool is a simple suicide screening tool developed to screen patient suicide risk, and the ASQ toolkit is designed for screening.
Changing Design to Change Healthcare.
Gardien Paul, et al (2021) says that, “Design plays a crucial role in changing healthcare. According to a recent article, experience design strategy can be used to improve healthcare quality and efficiency. Health care/system redesign involves making systematic changes to primary care practices and health systems to improve the quality, efficiency, and effectiveness of healthcare. A systematic review indicates that interventions to change healthcare professional behavior are haphazardly designed and poorly specified. The study suggests that clarity about methods for designing and specifying interventions is needed. Another study investigates the characteristics of successful organizational changes in healthcare. The study aims to investigate the factors that characterize successful organizational changes in healthcare. Finally, an article highlights the importance of informed design in changing healthcare. The design of a hospital or healthcare facility can affect the patients inside it, and informed design can change the way healthcare is delivered. In conclusion, design can play a significant role in changing healthcare, and there is a need for clarity about methods for designing and specifying interventions to improve healthcare quality and efficiency.
The effect of social crowding on self‐perceived health risks in healthcare services.
Shen Manqiong1, et al(2023) says that, ” investigates whether and how social crowding affects consumers’ self-perceived health risks in healthcare environments and its implications for healthcare service providers. The study includes one pilot study, seven laboratory experiments, and a field survey. Another study shows that social crowding increases the perception of health risks. Crowding is also associated with negative health outcomes, including exposure to risk factors associated with home injury, social tensions, and exposure to second-hand tobacco smoke. A literature review defines crowding and its effects on health, including the hazards associated with inadequate space within the dwelling for living, sleeping, and household activities. Crowding is also associated with negative mental health outcomes, such as psychological distress, suicidal ideation, and drug abuse. Finally, a study shows that crowding disproportionately affects individuals from lower socioeconomic status and catalyzes the transmission of infectious diseases. In conclusion, social crowding can affect self-perceived health risks in healthcare services and is associated with negative health outcomes. Healthcare service providers need to consider the implications of social crowding on consumers’ health risks and take measures to minimize the health risks associated with crowding.
A model for dual health care market with congestion differentiation.
Besancenot, et al (2023) says that, “The French market for specialist physician care has a dual legal structure, where physicians must exclusively work in sector 1 and charge regulated fees or in sector 2, where they can freely set their fees. Patient out-of-pocket payments in sector 2 are partially covered by private insurance. The primary differentiating factor between both sectors is the number of patients per specialist, which in turn directly affects the overall quality of the service provided. An equilibrium model was built to analyze both specialists’ decisions about which sector to work in and patients’ choice of physician and therefore sector. The model allowed the researchers to study the effect of changes in prices and economy-wide patient-to-specialist ratios on profits and patients’ utility associated with the services provided.
Hype News Diffusion and Risk of Misinformation: The Oz Effect in Health Care
Shi, Zijun, et al (2022) says that, ” The research topic of interest is the Oz Effect in health care, which refers to the amplification of hype news in health care without correction, leading to misinformation. The search results provide information on the impact of hype news on consumers and the risk of misinformation in health care. The study by Shi, Liu, and Srinivasan investigates the Oz Effect in health care and the risk of misinformation due to hype news diffusion. The study highlights the importance of mitigating the impact of hype news on consumers by providing accurate information and correcting misinformation. The study is available on various platforms, including ama.org, oatext.com, mark.hkust.edu.hk, and academia.edu. Another study investigates the impact of social media misinformation on public health and the significant association between spreading misinformation and the acceptance of public health misinformation. The study highlights the need for effective strategies to mitigate the impact of misinformation on public health. The search results provide insights into the Oz Effect in health care and the risk of misinformation due to hype news diffusion.
Conclusion:
The feasibility of modifying digital social prescribing for suicide bereavement support is examined in the first study. The second study looks on individual differences in risk propensity and the relationship between personality characteristics and actual risk-taking. The third study looks into the conflicting emotional effects of family members’ opposition to AI in healthcare. The study emphasises the significance of comprehending the impact of perceived risk of unfavourable outcomes on AI monitoring, which hasn’t been addressed in the literature. The fourth study looks at how social congestion affects how people in healthcare settings view their own health risks. The sixth study examines the Oz Effect in healthcare and the danger of inaccurate information spreading as a result of hyped news coverage. The sixth study examines federated learning for intelligent healthcare and offers.
Reference:
PARK, E. H. Werder, Karl2 werder@wiso.uni-koeln.de,Cao, Lan1, lcao@odu.eduRamesh, Balasubramaniam3, bramesh@gsu.edu. (2021) Why do Family Members Reject AI in Health Care? Competing Effects of Emotions. Journal of Management Information Systems, [s. l.], v. 39, n. 3, p. 765–792, 2022. DOI 10.1080/07421222.2022.2096550. Disponível em: https://discovery.ebsco.com/linkprocessor/plink?id=c77a1910-f3ec-32ff-9146-c52d70598617. Acesso em: 12 maio. 2023.
Aguinaldo, Laika D.2, Lanzillo, Elizabeth C.3, Horowitz, Lisa M.4 CHRISTENSEN LECLOUX, (2022)M Provider opinions of the acceptability of Ask Suicide-Screening Questions (ASQ) Tool and the ASQ Brief Suicide Safety Assessment (BSSA) for universal suicide risk screening in community healthcare: Potential barriers and necessary elements for future implementation. Journal of Behavioral Health Services & Research, [s. l.], v. 49, n. 3, p. 346–363, 2022. DOI 10.1007/s11414-022-09787-3. Disponível em: https://discovery.ebsco.com/linkprocessor/plink?id=d9e82f90-fd28-31df-abfc-286814a4550f. Acesso em: 12 maio. 2023.
BESANCENOT, D.; LAMIRAUD, K.; VRANCEANU, R. (2023) A model for dual health care market with congestion differentiation. Journal of Economics & Management Strategy, [s. l.], v. 32, n. 2, p. 400–423, 2023. DOI 10.1111/jems.12505. Disponível em: https://discovery.ebsco.com/linkprocessor/plink?id=8464bd05-cb24-3aa4-9b51-74d2c636a15b. Acesso em: 12 maio. 2023.
DINH C. NGUYEN QUOC-VIET PHAM2 vietpq@pusan.ac.kr ,PATHIRANA, PUBUDU N.3 pubudu.pathirana@deakin.edu ,MING DING4 ming.ding@data61.csiro.au ,SENEVIRATNE, ARUNA5 a.seneviratne@unsw.edu.au, ZIHUAI LIN6 zihuai.lin@sydney.edu.au , DOBRE, OCTAVIA7 odobre@mun.ca, WON-JOO HWANG8 wjhwang@pusan.ac.krFederated (2023) Learning for Smart Healthcare: A Survey. ACM Computing Surveys, [s. l.], v. 55, n. 3, p. 1–37, 2023. DOI 10.1145/3501296. Disponível em: https://discovery.ebsco.com/linkprocessor/plink?id=daf1237a-eec3-34bf-aa06-2ed0a39b2145. Acesso em: 12 maio. 2023.
FLEMING, M. D. (2023) Social prescribing and the search for value in health care. Economy & Society, [s. l.], v. 52, n. 2, p. 325–348, 2023. DOI 10.1080/03085147.2023.2175450. Disponível em: https://discovery.ebsco.com/linkprocessor/plink?id=83ba3ba9-56a5-322e-bd1b-1f483061032b. Acesso em: 12 maio. 2023.
GARDIEN, P.; DECKERS, E.(2023) Changing Design to Change Healthcare. Design Management Review, [s. l.], v. 34, n. 1, p. 28–35, 2023. DOI 10.1111/drev.12328. Disponível em: https://discovery.ebsco.com/linkprocessor/plink?id=4fbe0528-1abb-3f01-9e52-a284a0508650. Acesso em: 12 maio. 2023.
GIBSON, M. G.(2021) Exceptional Efficiencies: A Valuable Defense for Healthcare Mergers. Columbia Law Review, [s. l.], v. 122, n. 7, p. 1957–1995, 2022. Disponível em: https://discovery.ebsco.com/linkprocessor/plink?id=be6c8bc2-dacf-316e-9f04-4251c6531a20. Acesso em: 12 maio. 2023.
ROHOVA, M. 1Assoc. Prof. Dr. Medical University – Varna, Varna, Bulgaria,(2021) Inequalities in Health Care Services Utilization and the Impact of the COVID-19 Pandemic in Bulgaria. Izesstia, Journal of the Union of Scientists – Varna, Economic Sciences Series, [s. l.], v. 11, n. 3, p. 169–178, 2022. Disponível em: https://discovery.ebsco.com/linkprocessor/plink?id=ed2e4174-f143-385c-8b78-aba316350a5f. Acesso em: 12 maio. 2023.
SHEN, M.; GAO, S.; WANG, H. (2023) The effect of social crowding on self‐perceived health risks in healthcare services. Psychology & Marketing, [s. l.], v. 40, n. 4, p. 845–862, 2023. DOI 10.1002/mar.21771. Disponível em: https://discovery.ebsco.com/linkprocessor/plink?id=63a8cc23-0433-345c-a363-7dcb09410e87. Acesso em: 12 maio. 2023.
SHI, Z.; LIU X.; SRINIVASAN, K. (2022) Hype News Diffusion and Risk of Misinformation: The Oz Effect in Health Care. Journal of Marketing Research (JMR), [s. l.], v. 59, n. 2, p. 327–352, 2022. DOI 10.1177/00222437211044472. Disponível em: https://discovery.ebsco.com/linkprocessor/plink?id=de6728a2-07b9-3e69-85ce-03e90054f2a2. Acesso em: 12 maio. 2023.