Relevant bibliographies by topics / Health and Healthcare

Academic literature on the topic 'Health and Healthcare'

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Published: 18 May 2024

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Journal articles on the topic "Health and Healthcare"

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Innocent, David Chinaecherem, Chiagoziem Ogazirilem Emerole, Cosmas Nnadozie Ezejindu, Ugonma Winnie Dozie, Sophia Ifechidere Obani, Anthony Chinonso Uwandu-Uzoma, Chidozie Joachim Nwaokoro, et al. "Examination of Common Occupational Hazards among Healthcare Workers in a University Healthcare Center in Southeastern Nigeria." Health 14, no. 08 (2022): 833–52. http://dx.doi.org/10.4236/health.2022.148059.

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Hashemi, Fariba. "Dynamics of firm size in healthcare industry." Health 04, no. 03 (2012): 155–64. http://dx.doi.org/10.4236/health.2012.43024.

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Karpeta, Edyta, Karola Warzyszyńska, Piotr Małkowski, and Maciej Kosieradzki. "Healthcare Quality According to ICU Level of Care." Health 15, no. 12 (2023): 1352–65. http://dx.doi.org/10.4236/health.2023.1512088.

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Shelowi, Haila AL. "Health Policy and Planning in Health Management System." Journal of Medical Science And clinical Research 11, no. 11 (November 30, 2023): 89–93. http://dx.doi.org/10.18535/jmscr/v11i11.12.

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Health policy and planning play pivotal roles in the effective management of healthcare systems. These aspects encompass the formulation, implementation, and evaluation of strategies and regulations to optimize healthcare delivery. Robust health policies ensure equitable access, quality care, and cost-effectiveness, while planning entails resource allocation, infrastructure development, and workforce distribution. Successful health management systems hinge on evidence-based policies, stakeholder engagement, and adaptability to evolving health challenges. This abstract highlights the critical interplay between policy formulation and strategic planning, emphasizing their indispensable contributions to achieving efficient, accessible, and sustainable healthcare services.
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Jordanova, Malina. "Health: the tool to solve the healthcare dilemma." Journal scientific and applied research 1, no. 1 (June 6, 2012): 144–53. http://dx.doi.org/10.46687/jsar.v1i1.31.

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Brought to life by contemporary changes of our world, eHealth offers enormous possibili-ties. It is defined as the cost-effective and secure use of information and communication technologies in support of health health-related fields, including healthcare services, health surveillance, health literature, and health education by the World Health Assembly resolution on eHealth. It is impossible to have a detailed view of its potential as eHealth affects the entire health sector and is a viable tool to provide routine as well as specialized health services. It is able to improve both the access to and the standard of healthcare. The aim of this paper is to focus on how eHealth can help in closing the gap between need and demand in healthcare and thus solving the healthcare dilemma.
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Gagnon, Serge, and Laurent Chartier. "Health 3.0—The patient-clinician “arabic spring” in healthcare." Health 04, no. 02 (2012): 39–45. http://dx.doi.org/10.4236/health.2012.42008.

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Sayani, Hameedah, Immanuel Azaad Moonesar, Lama Zakzak, and Mona Mostafa Elsholkamy. "Factors Affecting Patient Satisfaction in the UAE’s Healthcare Sector." Health 15, no. 11 (2023): 1232–50. http://dx.doi.org/10.4236/health.2023.1511082.

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Frewin, Derek. "Improving health and healthcare." International Journal of Evidence-Based Healthcare 5, no. 4 (December 6, 2007): 369. http://dx.doi.org/10.1111/j.1479-6988.2007.00085.x.

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Orentlicher, David. "Healthcare, Health, and Income." Journal of Law, Medicine & Ethics 46, no. 3 (2018): 567–72. http://dx.doi.org/10.1177/1073110518804198.

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The medicalization model of poverty leads us to devote considerable resources to treating the healthcare problems caused by poverty while neglecting the root cause of those problems — the poverty itself. Treating symptoms rather than causes is far less effective than treating causes. When correctly understood, poverty is a major public health problem that needs to be addressed directly with effective anti-poverty programs. Only then can we properly serve the healthcare needs of the poor.
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Frewin, Derek. "Improving health and healthcare." International Journal of Evidence-Based Healthcare 5, no. 4 (December 2007): 369. http://dx.doi.org/10.1097/01258363-200712000-00002.

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Dissertations / Theses on the topic "Health and Healthcare"

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Kling, Rakel Nessa. "Promoting the health of healthcare workers : evaluating patient violence in healthcare." Thesis, University of British Columbia, 2007. http://hdl.handle.net/2429/32674.

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Introduction: The high rate of violence in the healthcare sector supports the need for greater prevention efforts. This study had two main objectives: 1) identify risk factors for violence province- wide and 2) investigate the effectiveness of a violence risk assessment system in reducing the risk of violence in an acute care hospital in British Columbia. Methods: Study 1: Data was extracted for a one-year period from the Workplace Health Indicator Tracking and Evaluation (WHITE ™) database for all employee reports of violent incidents for four of the six British Columbia Health Authorities. Risk factors for violence were identified through comparisons of incident rates (number of incidents/100,000 worked hours) by work characteristics, and by regression models. Study 2: Hospital violence incident rates (number of incidents/ 100,000 worked hours)were calculated pre, during and post implementation of the Alert System, a violence risk assessment system, at one acute care hospital. Then, using a retrospective case control study design, multivariable conditional logistic regression was used to model the effect of the Alert System (flag status yes or no) on the risk of a patient violent incident. Results: Study 1: Across health authorities, three groups at particularly high risk for violence were identified: very small healthcare facilities, the care aide occupation, and pediatric departments in acute care hospitals. Study 2: The violent incident rate decreased during the Alert System implementation period, but subsequently returned to pre-implementation levels. In the case-control analyses, patients flagged for violence were associated with an increased rather than decreased risk for violence. Conclusions: Study 1: The specific risk factors that put health care groups at an increased risk of violence should be examined so that targeted prevention or intervention efforts can be implemented. The identification of high-risk groups supports the importance of a province-wide surveillance system. Study 2: Although useful at identifying violent patients, the Alert System does not appear to provide the resources or procedures needed by health care workers to prevent a patient from progressing to a violent incident once flagged. These studies suggest that violence in healthcare should be studied and prevented using a multifaceted approach.
Medicine, Faculty of
Population and Public Health (SPPH), School of
Graduate
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Nakamoto, Ichiro. "Essays on Health, Healthcare, Job Insecurity and Health Outcomes." Scholar Commons, 2019. https://scholarcommons.usf.edu/etd/7865.

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This doctoral dissertation proposal is comprised of three separate chapters, all of which uses the nationally representative uniform survey Health and Retirement Survey (HRS) to examine the relationship between health, insurance, health care and health outcomes. Below, the brief introduction for each section is provided:  Chapter I: Medicare Part D and Patients' Well-being  Chapter II: Parent's Health Insurance and Informal Care  Chapter III: Job Insecurity and Health (with Dr. Ayyagari) In chapter I, I explore how Medicare Part D (MD) affects the well-being of the severely sick patients both in the short- and in the long- term. I employ difference-in-difference (DD) alongside the instrumental variable (IV) model. The estimated results imply MD significantly improves mental health and increases regular drug utilization for the elderly. However, it neither systematically improves out-of-pocket payment (OOP) nor improves mortality across all waves. This suggests that MD provides an efficient mechanism to improve mental health and drug utilization, but might not necessarily enhance survival rate and financial burden for vulnerable patients. Chapter II investigates the relationship between informal care provided by the children and the take-up of health insurance by the near-elderly and elderly parents, and how the correlation is influenced by parent’s Activities of Daily Living (ADLs) and Instrumental Activities of vii Daily Living (IADLs). The results indicate that when the endogeneity is controlled for, in-formal care systematically crowds out the take-up of private long-term care (LTC) insurance whereas “crowds in” the take-up of the total plan including supplement insurance plans (TSP). Nevertheless, the degree of both crowding-out and “crowding-in” effect is reduced when the severity of ADLs/IADLs disability level grows. Our study reflects (a) the strong demand for TSP and more additional health coverage within household budget line (b) and the potential gap between healthcare demands by the parents and the informal care provided by the children and the potential gap between the healthcare demands by the parents and the formal care covered by the insurance. Our estimates are robust to alternative measures of informal care. The final chapter III examines the causal effect of subjective job insecurity on health, using pooled ordinary least squares (OLS), fixed-effects (FE) and instrumental variable (IV) specifications. The estimate implies that the negative impact of job insecurity is more pronounced for certain outcomes such as mental health and the emergence of new health conditions. Job insecurity provides a powerful prediction on subsequent job displacement and real income loss. Sub-population such as low-employability/better-educated individuals or males responds more to job insecurity than their counterparts.
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Matthews, Bob. "Mixed ethnicity, health and healthcare experiences." Thesis, University of Birmingham, 2001. http://etheses.bham.ac.uk//id/eprint/1796/.

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The ethnic composition of Britain's population continues to change. This thesis explores the health and healthcare experiences of the fastest-growing sector of our population; people of mixed ethnicity. The thesis contextualises the research with reference to 'race' and ethnicity, immigration, demography and statistics. This research is based within a Foucauldian theoretical framework and utilises narrative data collection methods and an innovative analysis process, based on the construction of a series of metanarratives, to investigate the manner in which people of mixed ethnicity construct their identifies. It also seeks to explain how their ethnicity impacts both on health status and the nature of the mixed ethnicity healthcare experience in the NHS, particularly within the doctor/patient relationship. The findings from the research are discussed in relation to existing health policy initiatives and recommendations made for changes in the way in which the needs of people of mixed ethnicity are assessed, concluding that the present analytical categorisation are inadequate and in need of review. The research also concludes that doctors use their powerful position to suppress the discourse of health and mixed ethnicity.
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Abraham, Sarah Marie. "Essays on health and healthcare economics." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/120447.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Economics, 2018.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 147-156).
This thesis consists of three chapters on the economics of health and healthcare. The first and third chapters explore geographic variation in health outcomes within the United States. The second chapter focuses on empirical methods for obtaining causal estimates of treatment effects with an application to healthcare settings. In the first chapter I study geographic variation in health care utilization under two different insurance systems: traditional Medicare and employer-provided private insurance. For each system, I use patient migration as a source of identification combined with empirical Bayes methods to construct optimal linear forecasts for the causal effects of place on utilization. These place effects measure the causal differences in treatment intensity across areas. I find similar levels of variation in the causal place effects for the publicly and privately insured patients, with a correlation of .39 across the two systems. These findings emphasize that insurance systems are affecting the forces that drive the causal component of geographic variation in utilization. In the second chapter, Liyang Sun and I explore event studies, a model for estimating treatment effects using variation in the timing of treatment. Researchers often run fixed effects regressions for event studies that implicitly assume treatment effects are constant across cohorts first treated at different times. In this paper we show that these regressions produce causally uninterpretable estimands when treatment effects vary across cohorts. We propose alternative estimators that identify convex averages of the cohort-specific treatment effects, hence allowing for causal interpretation even under heterogeneous treatment effects. We illustrate the shortcomings of fixed effects estimators in comparison to our proposed estimators through an empirical application on the economic consequences of hospitalization. In the third chapter, Raj Chetty, Michael Stepner, Shelby Lin, Benjamin Scuderi, Nicholas Turner, Augustin Begeron, David Cutler and I use newly available administrative data to quantify the relationship between income and mortality in the United States. Although it is well known that there are significant differences in health and longevity between income groups, debate remains about the magnitudes and determinants of these differences. We use new data from 1.4 billion anonymous earnings and mortality records to construct more precise estimates of the relationship between income and life expectancy at the national level than was feasible in prior work. We then construct new local area (county and metro area) estimates of life expectancy by income group and identify factors that are associated with higher levels of life expectancy for low-income individuals. Our study yields four sets of results. First, higher income was associated with greater longevity throughout the income distribution. The gap in life expectancy between the richest 1% and poorest 1% of individuals was 14.6 years for men and 10.1 years for women. Second, inequality in life expectancy increased over time. Between 2001 and 2014, life expectancy increased by 2.34 years for men and 2.91 years for women in the top 5% of the income distribution, but increased by only 0.32 years for men and 0.04 years for women in the bottom 5%. Third, life expectancy varied substantially across local areas. For individuals in the bottom income quartile, life expectancy differed by approximately 4.5 years between areas with the highest and lowest longevity. Changes in life expectancy between 2001 and 2014 ranged from gains of more than 4 years to losses of more than 2 years across areas. Fourth, geographic differences in life expectancy for individuals in the lowest income quartile were significantly correlated with health behaviors such as smoking, but were not significantly correlated with access to medical care, physical environmental factors, income inequality, or labor market conditions. Life expectancy for low income individuals was positively correlated with the local area fraction of immigrants, fraction of college graduates, and local government expenditures. Additional information on this project is available at https: //healthinequality. org/.
by Sarah Marie Abraham.
Ph. D.
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Kessler, Aaron. "Transgender Experiences in Healthcare." Kent State University Honors College / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ksuhonors1588334197961745.

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Jani, Sonia. "Asthma, Related Healthcare Seeking, Disease Management, Health Care Access, Health Education, and Healthcare Provider Health Communication Among Immigrants and Asian Americans." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1627667134092486.

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Hidalgo, Stevan. "Healthcare expenditure vs healthcare outcomes a comparison of 25 world health organization member countries /." [Denver, Colo.] : Regis University, 2008. http://165.236.235.140/lib/SHidalgo2008.pdf.

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Youn, Ji Hee. "Modelling health and healthcare for an ageing population." Thesis, University of Sheffield, 2016. http://etheses.whiterose.ac.uk/13982/.

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Population ageing has received much attention as a contributing cause of spiralling healthcare expenditure. This study primarily aims to estimate the impact of population ageing on key diseases, and to develop a flexible modelling framework that can inform policy decisions. This research provides a proof-of-concept model where individual Discrete Event Simulation models for three diseases (heart disease, Alzheimer’s disease, and osteoporosis) were extended from existing published models to simulate the general UK population aged 45 years and older, and combined within a single model. Using external population projection data incorporating potential demographic changes, the methods for projecting future healthcare expenditures for the three diseases were demonstrated and the relative benefits of improving treatment of each of the diseases evaluated. Secondary outcomes include the development of a pragmatic literature search method which can be used for literature within diffuse topic areas, and a literature repository for future researchers to explore the existing literature on ageing and healthcare expenditure. Expenditure for the three diseases is projected to increase from £16 billion in 2012 to £28 billion in 2037. A key finding from this work is that the estimates of costs, quality-adjusted life years (QALYs), and the projected expenditure for healthcare services can differ when multiple diseases are modelled in a single model compared with the summed results from single disease models. This implies that policy decisions on the allocation and planning of healthcare resources based on the results from individual disease models can be different from those based on linked models. The novel approach of linking multiple disease models with correlations incorporated provides a new methodological option primarily for modellers who undertake research on comorbidities. It also has potential for wider applications in informing decisions on commissioning of healthcare services and long-term priority setting across diseases and healthcare programmes, hence ultimately contributing to the improvement of population health.
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Duell, Paul. "Assessing health literacy in a routine healthcare environment." Thesis, University of East Anglia, 2018. https://ueaeprints.uea.ac.uk/67703/.

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Background: Individuals with limited health literacy ability have poorer health outcomes compared with individuals with adequate health literacy. Health literacy ability is not assessed in routine healthcare environments in the UK. The objective of the thesis is to assess how healthcare professionals could identify an individual’s health literacy ability in daily practice. Methods: A systematic review of existing health literacy assessment instruments was undertaken to identify the optimal health literacy instrument for use in a clinical setting. The selected health literacy instrument was evaluated in a community pharmacy setting to provide an early indication of the feasibility for regular use. A theory based heuristic assessment instrument was developed and piloted as an alternative instrument for use in routine practice. Results: The systematic review identified the NVS instrument to be the most practical health literacy instrument to use. However, the early findings when used in practice indicated that there were barriers that could limit use. The preliminary findings of a heuristic assessment instrument indicate that recall of written potentially could be used. Conclusions: At present, there is no accepted practice to identify an individual’s health literacy ability in UK healthcare. Further research, with a larger sample size, into the use of heuristic indicators could identify a simple process to accurately assess health literacy ability that can be used in routine healthcare environments. Further work is also required to formulate more structured guidance on how to use the heuristic in consistent way so that the predictive ability demonstrated by the experienced pharmacists can be replicated by all.
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Airoldi, Mara. "Essays on healthcare priority setting for population health." Thesis, London School of Economics and Political Science (University of London), 2014. http://etheses.lse.ac.uk/916/.

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Healthcare priority setting is a major concern in most countries because healthcare represents a large and increasing public expenditure. Yet, there is not well established procedure that is consistently used to support those responsible for priority setting decisions. This dissertation consists of a review of the literature and five independent essays on healthcare priority setting, focusing on the value of formal analysis to support local healthcare planners in allocating a fixed budget. This dissertation makes both an intellectual and a practical contribution. The intellectual contribution is a synthesis of both economics and decision analysis insights. The review of the literature shows that tools grounded in health economics currently fail to contribute to local healthcare priority setting decisions because they are not practical. At the same time, tools grounded in (multi-criteria) decision analysis fail to incorporate the methodological advances of health economics and are hence theoretically weak. My thesis contributes to closing this gap. The practical contribution is that I design, and test the value of, a process and of particular value functions that can be used by local healthcare planners within their limited resources.
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Books on the topic "Health and Healthcare"

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Sherrow, Victoria. Universal healthcare. New York: Chelsea House, 2009.

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Iain, Mungall, and Cox Jim M. D, eds. Rural healthcare. Abingdon: Radcliffe Medical Press, 1999.

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Health, Nova Scotia Dept of. Healthcare update: Regionalization. [Halifax]: Dept. of Health, 1998.

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Humanizing healthcare reforms. London: Jessica Kingsley Publishers, 2013.

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Coghlan, David. Changing healthcare organisations. Dublin: Blackhall, 2003.

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John, Benington, ed. Leadership for healthcare. Bristol: Policy Press, 2010.

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Elizabeth, Layman, and American Health Information Management Association., eds. Principles of healthcare reimbursement. Chicago, Ill: American Health Information Management Association, 2006.

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Casto, Anne B. Principles of healthcare reimbursement. 2nd ed. Chicago, Ill: American Health Information Management Association, 2009.

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Kabene, Stefane M. Human resources in healthcare, health informatics, and healthcare systems. Hershey, PA: Medical Information Science Reference, 2010.

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1956-, Kabene Stefane M., ed. Human resources in healthcare, health informatics, and healthcare systems. Hershey, PA: Medical Information Science Reference, 2010.

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Book chapters on the topic "Health and Healthcare"

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Hilsenrath, Peter. "Health Insurance." In American Healthcare, 91–106. New York: Productivity Press, 2022. http://dx.doi.org/10.4324/9781003186137-8.

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Singleton, Stephen. "Public Health." In Rural Healthcare, 200–214. 2nd ed. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003302438-20.

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Kumar, Satish, Abha Mangal, and Daya Krishan Mangal. "Health Policy and Health System." In Healthcare System Management, 19–45. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3076-8_2.

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Unger, Felix. "Healthcare Financing." In Health is Wealth, 57–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-07738-2_6.

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Hummell, Jill. "Healthcare Relationships." In Health Practice Relationships, 195–202. Rotterdam: SensePublishers, 2014. http://dx.doi.org/10.1007/978-94-6209-788-9_23.

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Lennane, Simon. "Healthcare structures." In Creating Community Health, 59–72. London: Routledge, 2023. http://dx.doi.org/10.4324/9781003391784-5.

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Lawry, Tom. "Achieving Health Tequity." In Hacking Healthcare, 139–52. New York: Productivity Press, 2022. http://dx.doi.org/10.4324/9781003286103-15.

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Brittlebank, Andrew. "Rural Mental Health." In Rural Healthcare, 93–98. 2nd ed. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003302438-10.

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Wulfovich, Sharon, Homero Rivas, and Pedro Matabuena. "Drones in Healthcare." In Health Informatics, 159–68. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-61446-5_11.

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Harsanyi, Bennie E., David H. Wilson, Marguerite A. Daniels, Kathleen C. Allan, and John Anderson. "Healthcare Information Systems." In Health Informatics, 217–32. New York, NY: Springer New York, 1995. http://dx.doi.org/10.1007/978-1-4757-2428-8_19.

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Conference papers on the topic "Health and Healthcare"

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Verma, Anubha, Harsh Dhand, and Abhijit Shaha. "Healthcare kiosk next generation accessible healthcare solution." In 2008 10th International Conference on e-health Networking, Applications and Services (Healthcom). IEEE, 2008. http://dx.doi.org/10.1109/health.2008.4600135.

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Abbas, Raja Manzar, Noel Carroll, Ita Richardson, and Sarah Beecham. "Trust Factors in Healthcare Technology: A Healthcare Professional Perspective." In 11th International Conference on Health Informatics. SCITEPRESS - Science and Technology Publications, 2018. http://dx.doi.org/10.5220/0006594204540462.

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Thapa, Surya Bahadur, Aditi Rajput, Aradhana Gandhi, and Ramakrishnan Raman. "Mobile Health Applications towards Sustainable Healthcare: A Healthcare Professionals’Perspective." In 2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT). IEEE, 2023. http://dx.doi.org/10.1109/incacct57535.2023.10141765.

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Costello, Leesa, Julie Dare, Gloria Askander, and Marie-Louise McDermott. "How should online health-promoting communities address the health hazards of too much sitting?" In Annual Global Healthcare Conference. Global Science & Technology Forum (GSTF), 2014. http://dx.doi.org/10.5176/2251-3833_ghc14.12.

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Yu, W. D., and S. R. Jonnalagadda. "Semantic web and mining in healthcare." In HEALTHCOM 2006 8th International Conference on e-Health Networking, Applications and Services. IEEE, 2006. http://dx.doi.org/10.1109/health.2006.246449.

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Kuangtse Chien, Wanjiun Liao, I-Ching Hou, Chiahung Chien, Tzu-Hsiang Yang, Feipei Lai, ChungLee Niu, and A. Ho. "Location-aware healthcare in u-hospitals." In HEALTHCOM 2006 8th International Conference on e-Health Networking, Applications and Services. IEEE, 2006. http://dx.doi.org/10.1109/health.2006.246454.

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Yuan, Weiwei, Donghai Guan, Sungyoung Lee, and Heejo Lee. "Using Reputation System in Ubiquitous Healthcare." In 2007 9th International Conference on e-Health Networking, Application and Services. IEEE, 2007. http://dx.doi.org/10.1109/health.2007.381626.

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Guiqin Sun, Shenyi Tao, Yongqiang Lu, Yu Chen, Yuanchun Shi, Ni Rong, Rui Wang, and Xiaojuan Lu. "A low-cost community healthcare kiosk." In 2011 IEEE 13th International Conference on e-Health Networking, Applications and Services (Healthcom 2011). IEEE, 2011. http://dx.doi.org/10.1109/health.2011.6026763.

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Poropatich, Ronald, Holly H. Pavliscsak, Jeanette Rasche, Cynthia Barrigan, Robert A. Vigersky, Stephanie J. Fonda, and Amanda Bell. "Mobile healthcare in the US army." In Wireless Health 2010. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1921081.1921103.

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Kim, Ji-Hye, Mi-Hee Lee, and Kong-Keun Lee. "Analysis on Oral Health Associated Diabetes." In Healthcare and Nursing 2014. Science & Engineering Research Support soCiety, 2014. http://dx.doi.org/10.14257/astl.2014.72.07.

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Reports on the topic "Health and Healthcare"

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Burns, Marguerite, and John Mullahy. Healthy-Time Measures of Health Outcomes and Healthcare Quality. Cambridge, MA: National Bureau of Economic Research, August 2016. http://dx.doi.org/10.3386/w22562.

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Tipton, Kelley, Brian F. Leas, Emilia Flores, Christopher Jepson, Jaya Aysola, Jordana Cohen, Michael Harhay, et al. Impact of Healthcare Algorithms on Racial and Ethnic Disparities in Health and Healthcare. Agency for Healthcare Research and Quality (AHRQ), December 2023. http://dx.doi.org/10.23970/ahrqepccer268.

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Objectives. To examine the evidence on whether and how healthcare algorithms (including algorithm-informed decision tools) exacerbate, perpetuate, or reduce racial and ethnic disparities in access to healthcare, quality of care, and health outcomes, and examine strategies that mitigate racial and ethnic bias in the development and use of algorithms. Data sources. We searched published and grey literature for relevant studies published between January 2011 and February 2023. Based on expert guidance, we determined that earlier articles are unlikely to reflect current algorithms. We also hand-searched reference lists of relevant studies and reviewed suggestions from experts and stakeholders. Review methods. Searches identified 11,500 unique records. Using predefined criteria and dual review, we screened and selected studies to assess one or both Key Questions (KQs): (1) the effect of algorithms on racial and ethnic disparities in health and healthcare outcomes and (2) the effect of strategies or approaches to mitigate racial and ethnic bias in the development, validation, dissemination, and implementation of algorithms. Outcomes of interest included access to healthcare, quality of care, and health outcomes. We assessed studies’ methodologic risk of bias (ROB) using the ROBINS-I tool and piloted an appraisal supplement to assess racial and ethnic equity-related ROB. We completed a narrative synthesis and cataloged study characteristics and outcome data. We also examined four Contextual Questions (CQs) designed to explore the context and capture insights on practical aspects of potential algorithmic bias. CQ 1 examines the problem’s scope within healthcare. CQ 2 describes recently emerging standards and guidance on how racial and ethnic bias can be prevented or mitigated during algorithm development and deployment. CQ 3 explores stakeholder awareness and perspectives about the interaction of algorithms and racial and ethnic disparities in health and healthcare. We addressed these CQs through supplemental literature reviews and conversations with experts and key stakeholders. For CQ 4, we conducted an in-depth analysis of a sample of six algorithms that have not been widely evaluated before in the published literature to better understand how their design and implementation might contribute to disparities. Results. Fifty-eight studies met inclusion criteria, of which three were included for both KQs. One study was a randomized controlled trial, and all others used cohort, pre-post, or modeling approaches. The studies included numerous types of clinical assessments: need for intensive care or high-risk care management; measurement of kidney or lung function; suitability for kidney or lung transplant; risk of cardiovascular disease, stroke, lung cancer, prostate cancer, postpartum depression, or opioid misuse; and warfarin dosing. We found evidence suggesting that algorithms may: (a) reduce disparities (i.e., revised Kidney Allocation System, prostate cancer screening tools); (b) perpetuate or exacerbate disparities (e.g., estimated glomerular filtration rate [eGFR] for kidney function measurement, cardiovascular disease risk assessments); and/or (c) have no effect on racial or ethnic disparities. Algorithms for which mitigation strategies were identified are included in KQ 2. We identified six types of strategies often used to mitigate the potential of algorithms to contribute to disparities: removing an input variable; replacing a variable; adding one or more variables; changing or diversifying the racial and ethnic composition of the patient population used to train or validate a model; creating separate algorithms or thresholds for different populations; and modifying the statistical or analytic techniques used by an algorithm. Most mitigation efforts improved proximal outcomes (e.g., algorithmic calibration) for targeted populations, but it is more challenging to infer or extrapolate effects on longer term outcomes, such as racial and ethnic disparities. The scope of racial and ethnic bias related to algorithms and their application is difficult to quantify, but it clearly extends across the spectrum of medicine. Regulatory, professional, and corporate stakeholders are undertaking numerous efforts to develop standards for algorithms, often emphasizing the need for transparency, accountability, and representativeness. Conclusions. Algorithms have been shown to potentially perpetuate, exacerbate, and sometimes reduce racial and ethnic disparities. Disparities were reduced when race and ethnicity were incorporated into an algorithm to intentionally tackle known racial and ethnic disparities in resource allocation (e.g., kidney transplant allocation) or disparities in care (e.g., prostate cancer screening that historically led to Black men receiving more low-yield biopsies). It is important to note that in such cases the rationale for using race and ethnicity was clearly delineated and did not conflate race and ethnicity with ancestry and/or genetic predisposition. However, when algorithms include race and ethnicity without clear rationale, they may perpetuate the incorrect notion that race is a biologic construct and contribute to disparities. Finally, some algorithms may reduce or perpetuate disparities without containing race and ethnicity as an input. Several modeling studies showed that applying algorithms out of context of original development (e.g., illness severity scores used for crisis standards of care) could perpetuate or exacerbate disparities. On the other hand, algorithms may also reduce disparities by standardizing care and reducing opportunities for implicit bias (e.g., Lung Allocation Score for lung transplantation). Several mitigation strategies have been shown to potentially reduce the contribution of algorithms to racial and ethnic disparities. Results of mitigation efforts are highly context specific, relating to unique combinations of algorithm, clinical condition, population, setting, and outcomes. Important future steps include increasing transparency in algorithm development and implementation, increasing diversity of research and leadership teams, engaging diverse patient and community groups in the development to implementation lifecycle, promoting stakeholder awareness (including patients) of potential algorithmic risk, and investing in further research to assess the real-world effect of algorithms on racial and ethnic disparities before widespread implementation.
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Delgado, Alison, and Kevin Keene. Integrating Health and Energy Efficiency in Healthcare Facilities. Office of Scientific and Technical Information (OSTI), March 2021. http://dx.doi.org/10.2172/1773167.

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Rada, Gabriel. Can email communication between health professionals improve healthcare? SUPPORT, 2017. http://dx.doi.org/10.30846/1701154.

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The use of email as a medium for business and social communication is increasingly common. Healthcare professionals have been communicating via email since the early 1990s, for varying purposes. However, it is not clear what the impacts of emails in healthcare are when compared to other forms of communicating clinical information.
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Rada, Gabriel. Can email communication between health professionals improve healthcare? SUPPORT, 2017. http://dx.doi.org/10.30846/171501.

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The use of email as a medium for business and social communication is increasingly common. Healthcare professionals have been communicating via email since the early 1990s, for varying purposes. However, it is not clear what the impacts of emails in healthcare are when compared to other forms of communicating clinical information.
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Herrera, Cristian, and Andy Oxman. Does integration of primary healthcare services improve healthcare delivery and outcomes? SUPPORT, 2017. http://dx.doi.org/10.30846/170411.

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Primary healthcare in many low- and middle-income countries is organised through vertical programmes for specific health problems such as tuberculosis control or childhood immunisation. Vertical programmes can help deliver particular technologies or services, but may lead to service duplication and fragmentation. To address such problems, the World Health Organization and other organizations promote integration, where inputs, delivery, management and organization of particular service functions are brought together. Integration may involve adding a service to an existing vertical programme or full integration of services within routine healthcare delivery.
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Herrera, Cristian, Andy Oxman, and Shaun Treweek. Does integration of primary healthcare services improve healthcare delivery and outcomes? SUPPORT, 2017. http://dx.doi.org/10.30846/1704112.

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Primary healthcare in many low- and middle-income countries is organised through vertical programmes for specific health problems such as tuberculosis control or childhood immunisation. Vertical programmes can help deliver particular technologies or services, but may lead to service duplication and fragmentation. To address such problems, the World Health Organization and other organizations promote integration, where inputs, delivery, management and organization of particular service functions are brought together. Integration may involve adding a service to an existing vertical programme or full integration of services within routine healthcare delivery.
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Smith, N., M. Romanchikova, I. Partarrieu, E. Cooke, A. Lemanska, and S. Thomas. NMS 2018-2021 Life-sciences and healthcare project "Digital health: curation of healthcare data" - final report. National Physical Laboratory, November 2021. http://dx.doi.org/10.47120/npl.ms31.

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Anderson, G. Oscar. Getting to Know Americans Age 50+: Health & Healthcare. AARP Research, December 2014. http://dx.doi.org/10.26419/res.00091.004.

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Lampkin, Cheryl, Laura Mehegan, and G. Chuck Rainville. 2020 AARP Delirium and Brain Health Survey: Healthcare Providers. AARP Research, March 2020. http://dx.doi.org/10.26419/res.00376.002.

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