Journal articles: 'Health policy decisions'

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Meisel, Zachary F., Sarah E. Gollust, and David Grande. "Translating Research for Health Policy Decisions." Academic Medicine 91, no. 10 (October 2016): 1341–43. http://dx.doi.org/10.1097/acm.0000000000001182.

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Greipp, Mary Elizabeth. "Forces Driving Health Care Policy Decisions." Policy, Politics, & Nursing Practice 3, no. 1 (February 2002): 35–42. http://dx.doi.org/10.1177/152715440200300106.

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Attia, J. "Impact numbers in health policy decisions." Journal of Epidemiology & Community Health 56, no. 8 (August 1, 2002): 600–605. http://dx.doi.org/10.1136/jech.56.8.600.

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Witteman, Holly O., Anne-Sophie Julien, Ruth Ndjaboue, Nicole L. Exe, Valerie C. Kahn, Angela (Angie) Fagerlin, and Brian J. Zikmund-Fisher. "What Helps People Make Values-Congruent Medical Decisions? Eleven Strategies Tested across 6 Studies." Medical Decision Making 40, no. 3 (April 2020): 266–78. http://dx.doi.org/10.1177/0272989x20904955.

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Background. High-quality health decisions are often defined as those that are both evidence informed and values congruent. A values-congruent decision aligns with what matters to those most affected by the decision. Values clarification methods are intended to support values-congruent decisions, but their effects on values congruence are rarely evaluated. Methods. We tested 11 strategies, including the 3 most commonly used values clarification methods, across 6 between-subjects online randomized experiments in demographically diverse US populations ( n1 = 1346, n2 = 456, n3 = 840, n4 = 1178, n5 = 841, n6 = 2033) in the same hypothetical decision. Our primary outcome was values congruence. Decisional conflict was a secondary outcome in studies 3 to 6. Results. Two commonly used values clarification methods (pros and cons, rating scales) reduced decisional conflict but did not encourage values-congruent decisions. Strategies using mathematical models to show participants which option aligned with what mattered to them encouraged values-congruent decisions and reduced decisional conflict when assessed. Limitations. A hypothetical decision was necessary for ethical reasons, as we believed some strategies may harm decision quality. Later studies used more outcomes and covariates. Results may not generalize outside US-based adults with online access. We assumed validity and stability of values during the brief experiments. Conclusions. Failing to explicitly support the process of aligning options with values leads to increased proportions of values-incongruent decisions. Methods representing more than half of values clarification methods commonly in use failed to encourage values-congruent decisions. Methods that use models to explicitly show people how options align with their values offer more promise for helping people make decisions aligned with what matters to them. Decisional conflict, while arguably an important outcome in and of itself, is not an appropriate proxy for values congruence.

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Tolosana, Elvira Sanz. "Reducing health inequalities: the use of Health Impact Assessment on Rural Areas." Saúde e Sociedade 24, no. 2 (June 2015): 515–26. http://dx.doi.org/10.1590/s0104-12902015000200010.

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Health is greatly influenced by social, economic and political determinants. Accordingly, decisions influencing people's health do not concern only health services or 'health policies', but decisions in many different policy areas have their influence on these health determinants. Health Impact assessment (HIA) is a predictive tool to support decisions in policy-making. The ultimate goal of this framework is to maximize health gains and, as far as possible, to reduce health inequalities. HIA presents a commitment to ensure that the rural dimension is routinely considered as part of the making and implementing of policy. The aim of this paper is to review the use of HIA on rural areas. Conclusions: HIA shows its great potential to contribute to local authority decision making. The use of HIA was identified in 2 key areas: strategic planning (sustainable development, EU Common Agricultural Policy, Federal Farm Bill, land-use planning work); and in specific smaller scale projects (rural health service redesign proposal, accessing healthy food, transport, health care disparities, etc.).

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Garvelink, Mirjam M., Laura Boland, Krystal Klein, Don Vu Nguyen, Matthew Menear, Hilary L. Bekker, Karen B. Eden, et al. "Decisional Conflict Scale Findings among Patients and Surrogates Making Health Decisions: Part II of an Anniversary Review." Medical Decision Making 39, no. 4 (May 2019): 316–27. http://dx.doi.org/10.1177/0272989x19851346.

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Background. We explored decisional conflict as measured with the 16-item Decisional Conflict Scale (DCS) and how it varies across clinical situations, decision types, and exposure to decision support interventions (DESIs). Methods. An exhaustive scoping review was conducted using backward citation searches and keyword searches. Eligible studies were published between 1995 and March 2015, used an original experimental/observational research design, concerned a health-related decision, and provided DCS data. Dyads independently screened titles/abstracts and full texts, and extracted data. We performed narrative syntheses and calculated average or median DCS scores. Results. We included 246 articles reporting on 253 studies. DCS scores ranged from 2.4 to 79.7 out of 100. Highest baseline DCS scores were for care planning (44.8 ± 8.9, median = 47.0) and treatment decisions (32.5 ± 12.6, median = 31.9), in contexts of primary care (40.6 ± 18.3), and geriatrics (39.8 ± 11.2). Baseline scores were high among decision makers who were ill (33.2 ± 14.1, median = 30.2) or making decisions for themselves (33.4 ± 13.8, median = 32.0). Total DCS scores <25 out of 100 were associated with implementing decisions. Without DESIs, DCS scores tended to increase shortly after decision making (>37.4). After DESI use, DCS scores decreased short-term but increased or remained the same long-term (>6 months). Conclusions. DCS scores were highest at baseline and decreased after decision making. DESIs decreased decisional conflict immediately after decision making. The largest improvements after DESIs were in decision makers who were ill or made decisions for themselves. Further meta-analyses are needed for decision type, contexts, and interventions to inform hypotheses about the expected effects of DESIs, the best timing for measurement, and interpretation of DCS scores.

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Carlsson, Per. "Health technology assessment and priority setting for health policy in Sweden." International Journal of Technology Assessment in Health Care 20, no. 1 (January 2004): 44–54. http://dx.doi.org/10.1017/s0266462304000777.

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This article describes the development of health technology assessment (HTA) in Sweden, its influence on decision making, and its link with priority setting. Sweden has a well established governmental HTA body, the Swedish Council on Technology Assessment in Health Care (SBU), and an increasing number of regional/local HTA organizations. HTA has had an impact on clinical practice and is used to some extent in policy decisions. Several initiatives have now been taken to develop processes for open priority setting of health-care services. With the establishment of a new agency to undertake reimbursement decisions on pharmaceuticals, and greater patient and public involvement in decision making, it seems inevitable that HTA will play a more important role in priority setting in the near future.

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Byrne, Paul A. "Health Care Decisions." Linacre Quarterly 65, no. 3 (August 1998): 52–58. http://dx.doi.org/10.1080/00243639.1998.11878421.

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Stacey, Dawn, France Légaré, Laura Boland, Krystina B. Lewis, Marie-Chantal Loiselle, Lauren Hoefel, Mirjam Garvelink, and Annette O’Connor. "20th Anniversary Ottawa Decision Support Framework: Part 3 Overview of Systematic Reviews and Updated Framework." Medical Decision Making 40, no. 3 (April 2020): 379–98. http://dx.doi.org/10.1177/0272989x20911870.

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Introduction. The Ottawa Decision Support Framework (ODSF) has guided practitioners and patients facing difficult decisions for 20 years. It asserts that decision support interventions that address patients’ decisional needs improve decision quality. Purpose. To update the ODSF based on a synthesis of evidence. Methods. We conducted an overview of systematic reviews, searching 9 electronic databases. Eligible reviews included decisional needs assessments, decision support interventions, and decisional outcome measures guided by the ODSF. We extracted data and synthesized results narratively. Eight ODSF developers/expert users from 4 disciplines revised the ODSF. Results. Of 4656 citations, we identified 4 eligible reviews (>250 studies, >100 different decisions, >50,000 patients, 18 countries, 5 continents). They reported current ODSF decisional needs and their most frequent manifestations in the areas of inadequate knowledge/information, unclear values, decisional conflict/uncertainty, and inadequate support. They uncovered 11 new manifestations of 6 decisional needs. Using the Decisional Conflict Scale (DCS) to assess decisional needs, average scores were elevated at baseline and declined shortly after decision making, even without information interventions. Patient decision aids were superior to usual care in reducing total DCS scores and improving decision quality. We revised the ODSF by refining definitions of 6 decisional needs and adding new interventions to address 4 needs. We added a decision process outcome and eliminated secondary outcomes unlikely to improve across a range of decisions, retaining the implementation/continuance of the chosen option and appropriate use/costs of health services. Conclusions. We updated the ODSF to reflect the current evidence and identified implications for practice and further research.

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Strough, JoNell, Eric R. Stone, Andrew M. Parker, and Wändi Bruine de Bruin. "Perceived Social Norms Guide Health Care Decisions for Oneself and Others: A Cross-Sectional Experiment in a US Online Panel." Medical Decision Making 42, no. 3 (December 27, 2021): 326–40. http://dx.doi.org/10.1177/0272989x211067223.

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Background: Global aging has increased the reliance on surrogates to make health care decisions for others. We investigated the differences between making health care decisions and predicting health care decisions, self-other differences for made and predicted health care decisions, and the roles of perceived social norms, emotional closeness, empathy, age, and gender. Methods: Participants ( N = 2037) from a nationally representative US panel were randomly assigned to make or to predict a health care decision. They were also randomly assigned to 1 of 5 recipients: themselves, a loved one 60 y or older, a loved one younger than 60 y, a distant acquaintance 60 y or older, or a distant acquaintance younger than 60 y. Hypothetical health care scenarios depicted choices between relatively safe lower-risk treatments with a good chance of yielding mild health improvements versus higher-risk treatments that offered a moderate chance of substantial health improvements. Participants reported their likelihood of choosing lower- versus higher-risk treatments, their perceptions of family and friends’ approval of risky health care decisions, and their empathy. Results: We present 3 key findings. First, made decisions involved less risk taking than predicted decisions, especially for distant others. Second, predicted decisions were similar for others and oneself, but made decisions were less risk taking for others than oneself. People predicted that loved ones would be less risk taking than distant others would be. Third, perceived social norms were more strongly associated than empathy with made and predicted decisions. Limitations: Hypothetical scenarios may not adequately represent emotional processes in health care decision making. Conclusions: Perceived social norms may sway people to take less risk in health care decisions, especially when making decisions for others. These findings have implications for improving surrogate decision making. Highlights People made less risky health care decisions for others than for themselves, even though they predicted others would make decisions similar to their own. This has implications for understanding how surrogates apply the substituted judgment standard when making decisions for patients. Perceived social norms were more strongly related to decisions than treatment-recipient (relationship closeness, age) and decision-maker (age, gender, empathy) characteristics. Those who perceived that avoiding health care risks was valued by their social group were less likely to choose risky medical treatments. Understanding the power of perceived social norms in shaping surrogates’ decisions may help physicians to engage surrogates in shared decision making. Knowledge of perceived social norms may facilitate the design of decision aids for surrogates.

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Cataldo, Peter J. "Health Decisions or Majoritarian Health Care?" Linacre Quarterly 60, no. 1 (February 1993): 70–74. http://dx.doi.org/10.1080/20508549.1993.11878192.

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Hoefel, Lauren, Annette M. O’Connor, Krystina B. Lewis, Laura Boland, Lindsey Sikora, Jiale Hu, and Dawn Stacey. "20th Anniversary Update of the Ottawa Decision Support Framework Part 1: A Systematic Review of the Decisional Needs of People Making Health or Social Decisions." Medical Decision Making 40, no. 5 (July 2020): 555–81. http://dx.doi.org/10.1177/0272989x20936209.

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Background. The Ottawa Decision Support Framework (ODSF) has been used for 20 years to assess and address people’s decisional needs. The evidence regarding ODSF decisional needs has not been synthesized. Objectives. To synthesize evidence from ODSF-based decisional needs studies, identify new decisional needs, and validate current ODSF decisional needs. Methods. A mixed-studies systematic review. Nine electronic databases were searched. Inclusion criteria: studies of people’s decisional needs when making health or social decisions for themselves, a child, or a mentally incapable person, as reported by themselves, families, or practitioners. Two independent authors screened eligibility, extracted data, and quality appraised studies using the Mixed Methods Appraisal Tool. Data were analyzed using narrative synthesis. Results. Of 4532 citations, 45 studies from 7 countries were eligible. People’s needs for 101 unique decisions (85 health, 16 social) were reported by 2857 patient decision makers ( n = 36 studies), 92 parent decision makers ( n = 6), 81 family members ( n = 5), and 523 practitioners ( n = 21). Current ODSF decisional needs were reported in 2 to 40 studies. For 6 decisional needs, there were 11 new (manifestations): 1) information (overload, inadequacy regarding others’ experiences with options), 2) difficult decisional roles (practitioner, family involvement, or deliberations), 3) unrealistic expectations (difficulty believing outcome probabilities apply to them), 4) personal needs (religion/spirituality), 5) difficult decision timing (unpredictable), and 6) unreceptive decisional stage (difficulty accepting condition/need for treatment, powerful emotions limiting information processing, lacking motivation to consider delayed/unpredictable decisions). Limitations. Possible publication bias (only peer-reviewed journals included). Possible missed needs (non-ODSF studies, patient decision aid development studies, 3 ODSF needs added in 2006). Conclusion. We validated current decisional needs, identified 11 new manifestations of 6 decisional needs, and recommended ODSF revisions.

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Ashcroft, Rachelle. "Ontario’s Family Health Teams." Canadian Social Work Review 32, no. 1-2 (December 1, 2015): 117–32. http://dx.doi.org/10.7202/1034146ar.

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This article provides an overview of political decisions that led up to the implementation of the Ontario Family Health Team (FHT) model. FHTs have broadened primary health care in Ontario by bringing together family physicians with various interdisciplinary professionals. Political decisions have long influenced the shape and need for the FHT model. Knowledge of historically imbedded elements in the FHT model helps to strengthen current and future policy and decision-making. This article is informed by qualitative data collected from interviews with seven policy informants and 29 FHT leaders.

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Strauss, Ronald P. "Resource Allocation, Health Policy, and Rationing Craniofacial Care." Cleft Palate-Craniofacial Journal 32, no. 6 (November 1995): 515–19. http://dx.doi.org/10.1597/1545-1569_1995_032_0515_rahpar_2.3.co_2.

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The United States allocates health care without an overt system of rationing. This article analyzes the forces that guide resource allocation to craniofacial care. Various possible allocation systems are reviewed for how decision makers might evaluate proposed programs for legislative funding. Using a case-based exercise, readers are asked to weigh the potential costs and benefits of six health and social programs. These programs are also systematically examined for factors that are likely to affect resource allocation decisions. Eleven factors that affect decision-making are utilized in the analysis, ranging from the cost per client, to emotional or human interest content, of the proposed programs. Decisions about preventive programs are compared with those involving therapeutic programs. The allocation of resources to craniofacial programs, including those for children with rare major craniofacial conditions, is considered in the context of social justice and broad contemporary ethical and health care delivery issues.

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Blumenthal, David. "Decisions, Decisions: Why The Quality Of Medical Decisions Matters." Health Affairs 23, Suppl2 (January 2004): VAR—124—VAR—127. http://dx.doi.org/10.1377/hlthaff.var.124.

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Steele, Duncan, and Katherine Duthie. "Ethics of resource allocation in a public health emergency context." Healthcare Management Forum 34, no. 6 (September 28, 2021): 353–56. http://dx.doi.org/10.1177/08404704211047911.

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Resource allocation under non-emergency conditions is often challenging. Within the context of a Public Health Emergency (PHE), allocation decisions become significantly more difficult as decisions are often necessary on very short timelines, where relevant information (either evidence or information “on the ground”) is changing or incomplete, there is significant potential for harm, and resources are scarce, in unpredictable supply, and likely in high demand. An intentional value-based decision-making approach in such circumstances can clarify the values that ought to guide decisions, offering transparency and consistency, among other benefits. We use the example of vaccine allocation during the COVID-19 pandemic to explore value-based decision-making within a PHE context. We describe several core values that are relevant to PHE decision-making and outline their implications for approaches to vaccine allocation. While we focus on vaccine allocation, the values discussed are relevant to other system-level decisions in both emergency and non-emergency situations. Tips for leaders wishing to adopt a value-based approach to decision-making are offered.

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Clancy, Carolyn M., and Kelly Cronin. "Evidence-Based Decision Making: Global Evidence, Local Decisions." Health Affairs 24, no. 1 (January 2005): 151–62. http://dx.doi.org/10.1377/hlthaff.24.1.151.

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Witteman, Holly O., Ruth Ndjaboue, Gratianne Vaisson, Selma Chipenda Dansokho, Bob Arnold, John F. P. Bridges, Sandrine Comeau, et al. "Clarifying Values: An Updated and Expanded Systematic Review and Meta-Analysis." Medical Decision Making 41, no. 7 (September 25, 2021): 801–20. http://dx.doi.org/10.1177/0272989x211037946.

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Background Patient decision aids should help people make evidence-informed decisions aligned with their values. There is limited guidance about how to achieve such alignment. Purpose To describe the range of values clarification methods available to patient decision aid developers, synthesize evidence regarding their relative merits, and foster collection of evidence by offering researchers a proposed set of outcomes to report when evaluating the effects of values clarification methods. Data Sources MEDLINE, EMBASE, PubMed, Web of Science, the Cochrane Library, and CINAHL. Study Selection We included articles that described randomized trials of 1 or more explicit values clarification methods. From 30,648 records screened, we identified 33 articles describing trials of 43 values clarification methods. Data Extraction Two independent reviewers extracted details about each values clarification method and its evaluation. Data Synthesis Compared to control conditions or to implicit values clarification methods, explicit values clarification methods decreased the frequency of values-incongruent choices (risk difference, –0.04; 95% confidence interval [CI], –0.06 to –0.02; P < 0.001) and decisional conflict (standardized mean difference, –0.20; 95% CI, –0.29 to –0.11; P < 0.001). Multicriteria decision analysis led to more values-congruent decisions than other values clarification methods (χ2 = 9.25, P = 0.01). There were no differences between different values clarification methods regarding decisional conflict (χ2 = 6.08, P = 0.05). Limitations Some meta-analyses had high heterogeneity. We grouped values clarification methods into broad categories. Conclusions Current evidence suggests patient decision aids should include an explicit values clarification method. Developers may wish to specifically consider multicriteria decision analysis. Future evaluations of values clarification methods should report their effects on decisional conflict, decisions made, values congruence, and decisional regret.

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Xiao, Yue, Yingpeng Qiu, Liwei Shi, and Kun Zhao. "OP165 Health Technology Assessment And Public Health Priority Setting In China." International Journal of Technology Assessment in Health Care 35, S1 (2019): 36. http://dx.doi.org/10.1017/s0266462319001776.

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IntroductionSince 2009, China has initiated a national program on free provision of essential public health services. The national program has expanded both in terms of service categories and funding, showing China's great commitment to universal health coverage. However, with slowdown of public input in the health sector, the government decided to prioritize interventions and optimize reimbursement packages. Researchers in the China National Health Development Research Center (CNHDRC)—the Chinese national health technology assessment (HTA) agency were asked to design the tools to facilitate the decision process.MethodsWith multi-criteria decision analysis (MCDA) method, the researchers analyzed value dimensions in public health issues, and built an evidence matrix for the priority-setting decisions. Supported by HTA tools, they appraised interventions and services through literature review and field studies, and projected budget impact of potential adjustment decisions based on cost analysis results. A deliberative process of key stakeholder groups was taken, and their views were counted in making the final recommendations.ResultsBased on evidence review and scores of stakeholders’ judgment, two public health service interventions were recommended for removal, and another two for adjustment (one for merger, one for optimizing care pathway). Cost estimation and potential budgetary impact were also analyzed to support financial decisions.ConclusionsHTA and MCDA are key tools for defining the value criteria, evidence framework, and deliberative process for the essential public health program. However, lack of cost-effectiveness evidence hinders fine-tuned decisions on resource allocation. Continual health economic evaluation needs to be conducted in the near future.

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Persson, Emil, David Andersson, Lovisa Back, Thomas Davidson, Emma Johannisson, and Gustav Tinghög. "Discrepancy between Health Care Rationing at the Bedside and Policy Level." Medical Decision Making 38, no. 7 (September 10, 2018): 881–87. http://dx.doi.org/10.1177/0272989x18793637.

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Background. Whether doctors at the bedside level should be engaged in health care rationing is a controversial topic that has spurred much debate. From an empirical point of view, a key issue is whether there exists a behavioral difference between rationing at the bedside and policy level. Psychological theory suggests that we should indeed expect such a difference, but existing empirical evidence is inconclusive. Objective. To explore whether rationing decisions taken at the bedside level are different from rationing decisions taken at the policy level. Method. Behavioral experiment where participants ( n = 573) made rationing decisions in hypothetical scenarios. Participants (medical and nonmedical students) were randomly assigned to either a bedside or a policy condition. Each scenario involved 1 decision, concerning either a life-saving medical treatment or a quality-of-life improving treatment. All scenarios were identical across the bedside and policy condition except for the level of decision making. Results. We found a discrepancy between health care rationing at policy and bedside level for scenarios involving life-saving decisions, where subjects rationed treatments to a greater extent at the policy level compared to bedside level (35.6% v. 29.3%, P = 0.001). Medical students were more likely to ration care compared to nonmedical students. Follow-up questions showed that bedside rationing was more emotionally burdensome than rationing at the policy level, indicating that psychological factors likely play a key role in explaining the observed behavioral differences. We found no difference in rationing between bedside and policy level for quality-of-life improving treatments (54.6% v. 55.7%, P = 0.507). Conclusions. Our results indicate a robust “bedside effect” in the life-saving domain of health care rationing decisions, thereby adding new insights to the understanding of the malleability of preferences related to resource allocation.

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Whiteford, Harvey. "Can Research Influence Mental Health Policy?" Australian & New Zealand Journal of Psychiatry 35, no. 4 (August 2001): 428–34. http://dx.doi.org/10.1046/j.1440-1614.2001.00919.x.

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Objective: This paper describes the processes involved in policy development and implementation with examples of how this can be influenced by the outcomes of research. Method: The author draws on his experience in the development and implementation of Australia's National Mental Health Policy and on the literature describing public policy analysis. Results: A five-step process of problem identification, policy development, political decision, policy implementation and evaluation is described. This process identifies how issues are considered, adopted and implemented by governments. Conclusion: An understanding of this process can inform mechanisms by which scientific research can impact on the issues considered and the decisions made in each step of policy analysis and development.

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WOLF, ANDREW B., and GIDON FELSEN. "How public health policy can be informed by neuroscience." Behavioural Public Policy 3, no. 1 (May 7, 2018): 37–46. http://dx.doi.org/10.1017/bpp.2017.9.

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AbstractMany public policies are designed to counteract commonly made decisions that result in poor health. These policies have primarily been informed by the behavioural economics of decision making. Underappreciated in this conversation has been the perspective from neuroscience, despite its recent success – and the likelihood of future progress – in advancing our understanding of the neural basis for health-related decisions. Using tobacco control as an example, we provide a concise overview of how public health policies can and should be informed by neuroscience. We propose that such input can improve policies by increasing their effectiveness, improving screening efficiency and informing relevant ethical considerations. Finally, we recognise limitations and highlight roles that key stakeholders can play in incorporating neuroscientific evidence for the benefit of public policy.

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Toi, Alfred Kodjo, Ali Ben Charif, Claudia Lai, Gérard Ngueta, Karine V. Plourde, Dawn Stacey, and France Légaré. "Difficult Decisions for Older Canadians Receiving Home Care, and Why They Are So Difficult: A Web-Based Decisional Needs Assessment." MDM Policy & Practice 7, no. 2 (July 2022): 238146832211240. http://dx.doi.org/10.1177/23814683221124090.

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Background. Older adults receiving home care services often face decisions related to aging, illness, and loss of autonomy. To inform tailored shared decision making interventions, we assessed their decisional needs by asking about the most common difficult decisions, measured associated decisional conflict, and identified factors associated with it. Methods. In March 2020, we conducted a cross-sectional survey with a pan-Canadian Web-based panel of older adults (≥65 y) receiving home care services. For a difficult decision they had faced in the past year, we evaluated clinically significant decisional conflict (CSDC) using the 16-item Decisional Conflict Scale (score 0–100) with a >37.5 cutoff. To identify factors associated with CSDC, we performed descriptive, bivariable, and multivariable analyses using the stepwise selection method with an assumed entry and exit significance level of 0.15 and 0.20, respectively. Final model selection was based on the Bayesian information criterion. Results. Among 460 participants with an average age of 72.5 y, difficult decisions were, in order of frequency, about housing and safety (57.2%), managing health conditions (21.8%), and end-of-life care (8.3%). CSDC was experienced by 14.6% (95% confidence interval [CI]: 11.5%, 18.1%) of respondents on all decision points. Factors associated with CSDC included household size = 1 (OR [95% CI]: 1.81 [0.99, 3.33]; P = 0.27), household size = 3 (2.66 [0.78, 8.98]; P = 0.83), and household size = 4 (6.91 [2.23, 21.39]; P = 0.014); preferred option not matching the decision made (4.05 [2.05, 7.97]; P < 0.001); passive role in decision making (5.13 [1.78, 14.77]; P = 0.002); and lower quality of life (0.70 [0.57, 0.87]; P<0.001). Discussion. Some older adults receiving home care services in Canada experience CSDC when facing difficult decisions. Shared decision-making interventions could mitigate associated factors. Highlights This is the first study in Canada to assess the decisional needs of older adults receiving care at home and to identify their most common difficult decisions. Difficult decisions most frequently made were about housing and safety. The most significant decisional conflict was experienced by people making decisions about palliative care. When their quality-of-life score was low, older adults experienced clinically significant decision conflict.

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Oortwijn, Wija, and Philip Klein. "Addressing Health System Values in Health Technology Assessment: The Use of Evidence-Informed Deliberative Processes." International Journal of Technology Assessment in Health Care 35, no. 2 (2019): 82–84. http://dx.doi.org/10.1017/s0266462319000187.

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AbstractHealth technology assessment (HTA) is increasingly used around the globe to inform resource allocation decisions. Furthermore, the importance of using explicit and transparent criteria for coverage decision making in line with health system values has been acknowledged. However, the values of a health system are often not explicitly taken into account in the HTA process. This situation influences the allocation of scarce resources and could lead to a discord between the HTA outcome and the values of the health system. We describe how evidence-informed deliberative processes (EDPs) can help to improve this situation. EDPs are integrating two theoretical frameworks; multi-criteria decision-analysis and accountability for reasonableness. Through the use of EDPs, HTA agencies can ensure that health system values are more explicitly and consistently taken into account in the HTA process, enhancing the legitimacy of coverage decisions.

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Neumeyer, Juergen. "The Influence of Drug Policy on Transportation Policy Decisions." Journal of Drug Issues 32, no. 2 (April 2002): 567–72. http://dx.doi.org/10.1177/002204260203200215.

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Since about 1994, when the German Federal Constitutional Court effectively decriminalized the possession of small amounts of cannabis, a functional equivalent to criminal law has evolved in the form of traffic laws providing for the revocation of driving licenses in cases of cannabis possession. The historical and legal interpretations of this regulation are described, and its consequences are analyzed. It is postulated that, under the veil of traffic safety, harsh and repressive drug policies are being utilized to counteract legalization tendencies.

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Beryl, Louise L., Katharine A. S. Rendle, Meghan C. Halley, Katherine A. Gillespie, Suepattra G. May, Jennifer Glover, Peter Yu, Runi Chattopadhyay, and Dominick L. Frosch. "Mapping the Decision-Making Process for Adjuvant Endocrine Therapy for Breast Cancer." Medical Decision Making 37, no. 1 (July 10, 2016): 79–90. http://dx.doi.org/10.1177/0272989x16640488.

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Background. Studies show adjuvant endocrine therapy increases survival and decreases risk of breast cancer recurrence for hormone receptor–positive tumors. Yet studies also suggest that adherence rates among women taking this therapy may be as low as 50% owing largely to adverse side effects. Despite these rates, research on longitudinal patient decision making regarding this therapy is scant. Objective. We sought to map the decision-making process for women considering and initiating adjuvant endocrine therapy, paying particular attention to patterns of uncertainty and decisional change over time. Methods. A longitudinal series of semistructured interviews conducted at a multispecialty health care organization in Northern California with 35 newly diagnosed patients eligible for adjuvant endocrine therapy were analyzed. Analysis led to the identification and indexing of 3 new decision-making constructs—decisional phase, decisional direction, and decisional resolve—which were then organized using a visual matrix and examined for patterns characterizing the decision-making process. Results. Our data reveal that most patients do not make a single, discrete decision to take or not take hormone therapy but rather traverse multiple decisional states, characterized by 1) phase, 2) direction, and 3) strength of resolve. Our analysis tracks these decisional states longitudinally using a grayscale-coded matrix. Our data show that decisional resolve wavers not just when considering therapy, as the existing concept of decisional conflict suggests, but even after initiating it, which may signal future decisions to forgo therapy. Conclusions. Adjuvant endocrine therapy, like other chronic care decisions, has a longer decision-making process and implementation period. Thus, theoretical, empirical, and clinical approaches should consider further exploring the new concept and measurement of decisional resolve, as it may help to improve subsequent medication adherence.

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Marsh, Kevin, Axel Mühlbacher, Janine van Til, Christin Juhnke, Yookyung Christy Choi, Alejandra Duenas, Wolfgang Greiner, et al. "PP177 Health Preference Research In Europe: A Review Of Its Use." International Journal of Technology Assessment in Health Care 35, S1 (2019): 70. http://dx.doi.org/10.1017/s0266462319002691.

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IntroductionHealth Technology Assessment (HTA) and regulatory decisions involve value judgements. As patient groups, industry, and regulatory agencies conduct more preference studies to quantify these judgements, a better understanding of the methods and practices is needed. Currently, there is no systematic mapping of the use of preference data in Europe. This study aimed to identify (i) the use of quantitative preference data by all relevant HTA bodies and regulatory authorities of the European Union (EU) member states, and (ii) key standards and guidelines.MethodsThis study used a mixed method approach based on a systematic literature review, survey and subsequent interviews with decision makers and experts.ResultsA total of 62 survey responses were received. Many respondents reported that their agencies were responsible for supporting more than one type of decision, with 69.0 percent supporting approval decisions, 64.3 percent supporting reimbursement decisions, 61.9 percent supporting pricing decisions, and 64.2 percent supporting guideline development. Respondents reported that their agencies supported these decisions in multiple ways: 78.6 percent by assessing health technologies; 54.8 percent by appraising health technologies; 45.2 percent by compiling an HTA report; 7.1 percent by conducting primary research; 9.5 percent by conducting secondary research. More than 40 percent (42.9 percent) of agencies had the final say on one of the decisions of interest – approval, reimbursement, or pricing. Of the 31 countries studied, 71 percent (n = 22) used quantitative preference data in their reimbursement and pricing decisions. Of those, 86 percent (n = 19) used general population preferences to inform the estimation of quality-adjusted life years (QALY) as part of cost utility analysis.ConclusionsMuch of this use of preference data can be understood within the standard framework of economic analysis adopted by many HTA agencies; either in in the form of: standard ways to estimate QALYs; ways to broaden the impacts of technologies captured in the QALY; or ways to weigh health gain with other decision-making criteria, such as disease severity or innovativeness.

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Dobbins, Maureen, Rhonda Cockerill, Jan Barnsley, and Donna Ciliska. "FACTORS OF THE INNOVATION, ORGANIZATION, ENVIRONMENT, AND INDIVIDUAL THAT PREDICT THE INFLUENCE FIVE SYSTEMATIC REVIEWS HAD ON PUBLIC HEALTH DECISIONS." International Journal of Technology Assessment in Health Care 17, no. 4 (October 2001): 467–78. http://dx.doi.org/10.1017/s0266462301107026.

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Objective: To determine the extent to which systematic reviews of public health interventions influenced public health decisions and which factors were associated with influencing these decisions.Methods: This cross-sectional follow-up survey evaluated the use of five systematic reviews in public health decision making. Independent variables included characteristics of the innovation, organization, environment, and individual. Primary data were collected using a telephone survey and a self-administered organizational demographics questionnaire. Public health decision makers in all 41 public health units in Ontario were invited to participate in the study. Multiple linear regression analyses on the five program decisions were conducted.Results: The systematic reviews were perceived as having the greatest amount of influence on decisions related to program justification and program planning, and the least influence on program evaluation decisions. The greater the perception that one's organization valued the use of research evidence for decision making and that ongoing training in the critical appraisal of research literature was provided, the greater the perception of the influence the systematic review had on public health decisions.Conclusions: Organizational characteristics are important predictors of the use of systematic reviews in public health decision making. Future dissemination strategies need to promote the value of using systematic reviews for program decision making as well as promote ongoing training in critical appraisal among intended users in Ontario.

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Schlander, Michael, and Jeffrey Richardson. "QALYs In Health Resource Usage Decisions." Health Affairs 41, no. 4 (April 1, 2022): 609–10. http://dx.doi.org/10.1377/hlthaff.2021.01926.

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Koehler, Kirsten, Megan Latshaw, Thomas Matte, Daniel Kass, Howard Frumkin, Mary Fox, Benjamin F. Hobbs, Marsha Wills-Karp, and Thomas A. Burke. "Building Healthy Community Environments: A Public Health Approach." Public Health Reports 133, no. 1_suppl (November 2018): 35S—43S. http://dx.doi.org/10.1177/0033354918798809.

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Environmental quality has a profound effect on health and the burden of disease. In the United States, the environment-related burden of disease is increasingly dominated by chronic diseases. At the local level, public health practitioners realize that many policy decisions affecting environmental quality and health transcend the authorities of traditional health department programs. Healthy decisions about the built environment, including housing, transportation, and energy, require broad collaborative efforts. Environmental health professionals have an opportunity to address the shift in public health burden toward chronic diseases and play an important role in the design of healthy communities by bringing data and tools to decision makers. This article provides a guide for community leaders to consider the public health effects of decisions about the built environment. We present a conceptual framework that represents a shift from compartmentalized solutions toward an inclusive systems approach that encourages partnership across disciplines and sectors. We discuss practical tools to assist with environmental decision making, such as Health Impact Assessments, environmental public health tracking, and cumulative risk assessment. We also identify priorities in research, practice, and education to advance the role of public health in decision making to improve health, such as the Health Impact Assessment, as a core competency for environmental health practitioners. We encourage cross-disciplinary communication, research, and education that bring the fields of planning, transportation, and energy in closer collaboration with public health to jointly advance the systems approach to today’s environmental challenges.

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Nowell, William Benjamin, Shilpa Venkatachalam, Christine Stake, Erik Harden, Liana Fraenkel, Ellen Peters, and Thomas W. Concannon. "Identifying patient decisions and related information needs during decision making related to total knee arthroplasty." Journal of Comparative Effectiveness Research 9, no. 16 (November 2020): 1153–66. http://dx.doi.org/10.2217/cer-2020-0109.

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Aim: Research regarding decisions patients make about total knee arthroplasty, apart from having the procedure or not, are limited. Understanding patient decision making and related information needs is essential for shared decision making. Methods: Focus groups with an online community-based sample identified decisions about total knee arthroplasty beyond the decision to have the surgery itself. An online survey was used to determine relative importance of five major decisions and evaluate related information available. Results: Patients did not feel they have enough information to make important decisions of surgeon, device type, surgical approach, facility, or timing, for their total knee arthroplasty. Conclusion: Although further research is needed to generalize these findings, physicians should consider these questions during shared decision making with patients considering total knee arthroplasty.

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Braithwaite, R. Scott, and Mark S. Roberts. "Are Discount Rates Too High? Population Health and Intergenerational Equity." Medical Decision Making 41, no. 2 (January 13, 2021): 245–49. http://dx.doi.org/10.1177/0272989x20979816.

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Increasing attention is being paid to policy decisions in which shorter-term benefits may be eclipsed by longer-term harms, such as environmental damage. Health policy decisions have largely been spared this scrutiny, even though they too may contribute to longer-term harms. Any healthy population or society must sustain itself through reproduction, and therefore, transgenerational outcomes should be of intrinsic importance from a societal perspective. Yet, the discount rates typically employed in cost-effectiveness analyses have the effect of minimizing the importance of transgenerational health outcomes. We argue that, because cost-effectiveness analysis is based on foundational axioms of decision theory, it should value transgenerational outcomes consistently with those axioms, which require discount rates substantially lower than 3%. We discuss why such lower rates may not violate the Cretin-Keeler paradox.

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Whitney, Simon N., Margaret Holmes-Rovner, Howard Brody, Carl Schneider, Laurence B. McCullough, Robert J. Volk, and Amy L. McGuire. "Beyond Shared Decision Making: An Expanded Typology of Medical Decisions." Medical Decision Making 28, no. 5 (June 30, 2008): 699–705. http://dx.doi.org/10.1177/0272989x08318465.

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Liberati, Alessandro, Trevor A. Sheldon, and H. David Banta. "EUR-ASSESS Project Subgroup Report on Methodology:Methodological Guidance for the Conduct of Health Technology Assessment." International Journal of Technology Assessment in Health Care 13, no. 2 (1997): 186–219. http://dx.doi.org/10.1017/s0266462300010369.

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Health technology assessment (HTA) is primarily concerned with the consequences (benefits and costs) of health care and health policy decisions. Because decision making is complex and outcomes are often uncertain, it is helpful to attempt to assess the consequences. The quality of decisions can be improved by a process that provides a consistent framework for identifying and assessing health technologies.

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Lognon, Tania, Amédé Gogovor, Karine V. Plourde, Paul Holyoke, Claudia Lai, Emmanuelle Aubin, Kathy Kastner, et al. "Predictors of Decision Regret among Caregivers of Older Canadians Receiving Home Care: A Cross-Sectional Online Survey." MDM Policy & Practice 7, no. 2 (July 2022): 238146832211163. http://dx.doi.org/10.1177/23814683221116304.

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Background. In Canada, caregivers of older adults receiving home care face difficult decisions that may lead to decision regret. We assessed difficult decisions and decision regret among caregivers of older adults receiving home care services and factors associated with decision regret. Methods. From March 13 to 30, 2020, at the outbreak of the COVID-19 pandemic, we conducted an online survey with caregivers of older adults receiving home care in the 10 Canadian provinces. We distributed a self-administered questionnaire through Canada’s largest and most representative private online panel. We identified types of difficult health-related decisions faced in the past year and their frequency and evaluated decision regret using the Decision Regret Scale (DRS), scored from 0 to 100. We performed descriptive statistics as well as bivariable and multivariable linear regression to identify factors predicting decision regret. Results. Among 932 participants, the mean age was 42.2 y (SD = 15.6 y), and 58.4% were male. The most frequently reported difficult decisions were regarding housing and safety (75.1%). The mean DRS score was 28.8/100 (SD = 8.6). Factors associated with less decision regret included higher caregiver age, involvement of other family members in the decision-making process, wanting to receive information about the options, and considering organizations interested in the decision topic and health care professionals as trustworthy sources of information (all P < 0.001). Factors associated with more decision regret included mismatch between the caregiver’s preferred option and the decision made, the involvement of spouses in the decision-making process, higher decisional conflict, and higher burden of care (all P < 0.001). Discussion. Decisions about housing and safety were the difficult decisions most frequently encountered by caregivers of older adults in this survey. Our results will inform future decision support interventions. Highlights This is one of the first studies to assess decision regret among caregivers of older adults receiving home and community care services and to identify their most frequent difficult decisions. Difficult decisions were most frequently about housing and safety. Most caregivers of older adults in all 10 provinces of Canada experienced decision regret. Factors associated with less decision regret included higher caregiver age, the involvement of other family members in the decision-making process, wanting to receive information about the options, considering organizations interested in the decision topic, and health care professionals as trustworthy sources of information. Factors associated with more decision regret included mismatch between the caregiver’s preferred option and the decision made, the involvement of spouses in the decision-making process, higher decisional conflict, and higher burden of care.

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Elidor, Hélène, Ali Ben Charif, Codjo Djignefa Djade, Rhéda Adekpedjou, and France Légaré. "Decision Regret among Informal Caregivers Making Housing Decisions for Older Adults with Cognitive Impairment: A Cross-sectional Analysis." Medical Decision Making 40, no. 4 (May 2020): 416–27. http://dx.doi.org/10.1177/0272989x20925368.

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Background. Informal caregivers are regularly faced with difficult housing decisions for older adults with cognitive impairment. They often regret the decision they made. We aimed to identify factors associated with decision regret among informal caregivers engaging in housing decisions for cognitively impaired older adults. Methods. We performed a secondary analysis of cross-sectional data collected from a cluster-randomized trial. Eligible participants were informal caregivers involved in making housing decisions for cognitively impaired older adults. Decision regret was assessed after caregivers’ enrollment in the study using the Decision Regret Scale (DRS), scored from 0 to 100. We used a conceptual framework of potential predictors of regret to identify independent variables. We performed multilevel analyses using a mixed linear model by estimating fixed effects (β) and 95% confidence intervals (CIs). Results. The mean (SD) DRS score of 296 informal caregivers (mean [SD] age, 62 [12] years) was 12.4 (18.4). Factors associated with less decision regret were having a college degree compared to primary education (β [95% CI]: –11.14 [–18.36, –3.92]), being married compared to being single (–5.60 [–10.05, –1.15]), informal caregivers’ perception that a joint process occurred (–0.14 [–0.25, –0.02]), and older adults’ not having a specific housing preference compared to preferring to stay at home (–4.13 [–7.40, –0.86]). Factors associated with more decision regret were being retired compared to being a homemaker (7.74 [1.32, 14.16]), higher burden of care (0.14 [0.05, 0.22]), and higher decisional conflict (0.51 [0.34, 0.67]). Limitations. Our analysis may not illustrate all predictors of decision regret among informal caregivers. Conclusions. Our findings will allow risk-mitigation strategies for informal caregivers at risk of experiencing regret.

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Chatterjee, Somnath, and Arindam Laha. "Decision on Institutional Choice in the Healthcare Sector: Micro-empirical Evidences from West Bengal (India)." Journal of Health Management 21, no. 1 (February 25, 2019): 177–91. http://dx.doi.org/10.1177/0972063418822214.

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An attempt has been made in this article to shed some light on the decisions of patients on their institutional choice in accessing healthcare services within micro empirical framework in the state of West Bengal, India. The determinants of such decisions are also recognized in this article. In conscientious, statistical methods are anticipated to frame the socio-economic and decisional variables, that influence the decision. Principal component analysis and ordered probit analysis have been deployed to scrutinize the same. It is observed that the economic status of the patients, their level of schooling, their income and some other decision variables have a strong influence on their access of healthcare institutions. The study has projected that intra-category and inter-category competition among available alternative service providers needs to be introduced and social entrepreneurship model in state-owned hospitals can be pioneered with due considerations.

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Ohinmaa, Arto, and David Hailey. "Telemedicine, Outcomes and Policy Decisions." Disease Management and Health Outcomes 10, no. 5 (2002): 269–76. http://dx.doi.org/10.2165/00115677-200210050-00001.

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Maia, Maria. "OP318 Health Technology Assessment And Decision-Making Processes: The Purchase Of Magnetic Resonance Imaging Technology." International Journal of Technology Assessment in Health Care 37, S1 (December 2021): 13. http://dx.doi.org/10.1017/s0266462321000970.

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IntroductionMedical devices play an essential role in health care, but they are also a leading causes of increasing healthcare expenditures. The purchase of technologies and the determination of how and when they should be used are among the most important decisions made by decision-makers, at the institutional level.The present research focuses on the Portuguese health system and sheds light on the characterization of decision-making process by those involved in Magnetic Resonance Imaging (MRI) purchases.MethodsTo characterize the decision-making process, results from forty questionnaires and twenty-seven semi-structured interviews with key decision-makers were merged, using a mixed method approach. To assess competences for decision-making, a questionnaire was applied, and Exploratory and Confirmatory Factorial Analysis conducted.ResultsCost and suppliers’ characteristics are seen as the most important indicators to guide decisions. The decision is undertaken by a committee, in a bottom-up process, characterized by a bounded rationality, influenced by intuition and a consultant decision-maker. The reasoning and justification for selection of the committee members is unclear. The decision process is considered to be bureaucratic, time-consuming and long. Patients are negatively perceived as stakeholders in the process. Few studies were performed (mostly related to the workload of the Radiology Department) to support the decision and no national or international health technology assessment (HTA) study was used in the process, to guide decisions. Decision-makers have limited knowledge and training in areas of decision-making in the areas of health informatics, health economics and especially HTA. This may limit their ability to truly understand the future implications of their purchase decisions.ConclusionsTo foster HTA in decision-making processes, recommendations are made, in particular, to: (i) establish an HTA in-house unit, able to carry out studies considering the hospital context and aiming to inform managerial local decisions (ii) promote a team comprised of technology assessment multidisciplinary researchers but also professionals from the health institution able to carry out HTA studies (iii) foster common languages and values to increase uptake of HTA studies.

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Lancaster, Kari, and Tim Rhodes. "What prevents health policy being ‘evidence-based’? New ways to think about evidence, policy and interventions in health." British Medical Bulletin 135, no. 1 (September 2020): 38–49. http://dx.doi.org/10.1093/bmb/ldaa026.

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Abstract Background Evidence-based policy decision-making is a dominant paradigm in health but realizing this ideal has proven challenging. Sources of data This paper conceptually maps health policy, policy studies and social science literature critically engaged with evidence and decision-making. No new data were generated or analysed in support of this review. Areas of agreement Barriers to evidence-based policy have been documented, with efforts made to increase the uptake of evidence. Areas of controversy Evident complexities have been regarded as a problem of translation. However, this assumes that policy-making is a process of authoritative choice, and that ‘evidence’ is inherently valuable policy knowledge, which has been critiqued. Growing points Alternative accounts urge consideration of how evidence comes to bear on decisions made within complex systems, and what counts as evidence. Areas timely for developing research An ‘evidence-making intervention’ approach offers a framework for conceptualizing how evidence and interventions are made relationally in practices, thus working with the politics and contingencies of implementation and policy-making.

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Grigg, Margaret, Helen Herrman, Carol Harvey, and Ruth Endacott. "Factors influencing triage decisions in mental health services." Australian Health Review 31, no. 2 (2007): 239. http://dx.doi.org/10.1071/ah070239.

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The aim of the study was to identify the factors influencing the timing of an assessment after contact with a triage program in a communitybased area mental health service in Australia. Triage decisions apparently were influenced by several groups of factors: patient characteristics; the source and mode of the contact with triage; and to a large extent by mental health service factors including the training, supervision and support of triage workers and the perceived availability of an assessment. While demand factors such as patient characteristics influenced the triage decision, supply factors also played an important role.

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Pannell, Jeff. "Business judgment and health-care decisions." American Journal of Health-System Pharmacy 42, no. 2 (February 1, 1985): 280. http://dx.doi.org/10.1093/ajhp/42.2.280.

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Klein, Catherine Nichols. "Guiding patients toward rational health decisions." American Journal of Health-System Pharmacy 53, no. 19 (October 1, 1996): 2273. http://dx.doi.org/10.1093/ajhp/53.19.2273.

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Coates, P. M. "Evidence-Based Reviews in Support of Health Policy Decisions." JNCI Journal of the National Cancer Institute 99, no. 14 (July 10, 2007): 1059. http://dx.doi.org/10.1093/jnci/djm049.

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Sleath, Betsy, and T. Donald Rucker. "Consumer Participation in Health Policy Decisions: Empowerment or Puffery?" Journal of Health Care for the Poor and Underserved 12, no. 1 (2001): 35–49. http://dx.doi.org/10.1353/hpu.2010.0571.

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Tunis, S. R. "Lack of evidence for clinical and health policy decisions." BMJ 347, dec16 3 (December 16, 2013): f7155. http://dx.doi.org/10.1136/bmj.f7155.

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Jennings, Carole P. "Power that Empowers: Health Professionals Guiding Public Policy Decisions." Policy, Politics, & Nursing Practice 1, no. 4 (November 2000): 243–44. http://dx.doi.org/10.1177/152715440000100401.

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Henry, David, and Ruth Lopert. "Pharmacoeconomics and policy decisions: The australian health care system." Clinical Therapeutics 21, no. 5 (May 1999): 909–15. http://dx.doi.org/10.1016/s0149-2918(99)80012-5.

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Benson, Bruce L., and David W. Rasmussen. "The Context of Drug Policy: An Economic Interpretation." Journal of Drug Issues 28, no. 3 (July 1998): 681–99. http://dx.doi.org/10.1177/002204269802800307.

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Economics can be used to analyze public sector decision making because individuals make these decisions within a framework of incentives and constraints that are a product of individual preferences and institutional structure. Considering the emphasis on law enforcement in U.S. drug policy in this context, this paper presents an analysis of the incentives and constraints affecting drug policy that explains a reluctance to change the policy even in the face of considerable evidence that some reforms could be cost effective. Two specific incentives for an emphasis on enforcement are presented. First, police agencies have an incentive to allocate more resources to drug enforcement due to the factors that determine police budgets. Second, asset forfeiture laws give police agencies a direct monetary reward for making drug arrests. We conclude that understanding drug policy requires an examination of the incentives and constraints that affect the behavior of those who are responsible for policy development.

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Kapiriri, Lydia, Rob Baltussen, and Wija Oortwijn. "Implementing evidence-informed deliberative processes in health technology assessment: a low income country perspective." International Journal of Technology Assessment in Health Care 36, no. 1 (2020): 29–33. http://dx.doi.org/10.1017/s0266462319003398.

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AbstractThe purpose of this paper is to discuss the potential feasibility and utility of evidence-informed deliberative processes (EPDs) in low income country (LIC) contexts. EDPs are implemented in high and middle income countries and thought to improve the quality, consistency, and transparency of decisions informed by health technology assessment (HTA). Together these would ultimately improve the legitimacy of any decision making process. We argue—based on our previous work and in light of the priority setting literature—that EDPs are relevant and feasible within LICs. The extreme lack of resources necessitates making tough decisions which may mean depriving populations of potentially valuable health technologies. It is critical that the decisions and the decision making bodies are perceived as fair and legitimate by the people that are most affected by the decisions. EDPs are well aligned with the political infrastructure in some LICs, which encourages public participation in decision making. Furthermore, many countries are committed to evidence-informed decision making. However, the application of EDPs may be hampered by the limited availability of evidence of good quality, lack of interest in transparency and accountability (in some LICs), limited capacity to conduct HTA, as well as limited time and financial resources to invest in a deliberative process. While EDPs would potentially benefit many LICs, mitigating the identified potential barriers would strengthen their applicability. We believe that implementation studies in LICs, documenting the contextualized enablers and barriers will facilitate the development of context specific improvement strategies for EDPs.

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Journal articles on the topic 'Health policy decisions'

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