Patient decision aids to support clinical decision making: evaluating the decision or the outcomes of the decision.

Decision aids (DAs) are tools to support patients make informed health decisions with their practitioner. They aim to improve patient knowledge of options, incorporate patient preferences and values, and increase patient involvement in health decision making. Increasingly, the debate about DAs concerns how they should be implemented in practice, with the view that DAs are superior to usual clinical care in facilitating health decisions. The authors challenge this view and suggest that DA research has focused on measures of decision process, leaving the effects on the outcome of the decision relatively unknown. It is still unclear in which conditions DAs are better for patient health and well-being than clinician-led decisions. The authors present a new randomized design to examine the effects of DA-supported patient choice on patient-centered outcomes to identify where DAs are best implemented in clinical practice. In this design, patients are randomized to 1 of 4 arms: intervention A, intervention B, choice of either intervention supported by a clinician, or choice of either intervention supported by a decision aid. Health and quality of life measured over the long term are presented as the primary outcomes. The authors propose that this design will allow the proper assessment of different modes of decision making.     

Design of effective health decision support systems: towards automating the health administrators's decision processes.

There has been very little evidence to suggest the effective use of, and demand for, computer-based systems by health care administrators to support their increasingly complex decision-making activities. This is so, despite a considerable amount of research attention paid to examining the efficiency and power of mathematical models, and the application of increasingly sophisticated Management Science Techniques to problems encountered within the health care system over the last several years. In recent times, advancing computer technology: 1) promises to close the gap between theory and practice, 2) creates a new perspective for computerized decision support in health care organisations, and 3) points to the need for new directions in research. This paper highlights the factors that are critical to the success of interfacing between health managerial decision-makers and effective computerized decision aids.     

Preparing for a decision support system

The increasing pressure to reduce costs and improve outcomes is driving the health care industry to view information as a competitive advantage. Timely information is required to help reduce inefficiencies and improve patient care. Numerous disparate operational or transactional information systems with inconsistent and often conflicting data are no longer adequate to meet the information needs of integrated care delivery systems and networks in competitive managed care environments. This article reviews decision support system characteristics and describes a process to assess the preparedness of an organization to implement and use decision support systems to achieve a more effective, information-based decision process. Decision support tools included in this article range from reports to data mining.     

A proposal for the development of national certification standards for patient decision aids in the US

Efforts to implement the use of patient decision aids to stimulate shared decision making are gaining prominence. Patient decision aids have been designed to help patients participate in making specific choices among health care options. Because these tools clearly influence decisions, poor quality, inaccurate or unbalanced presentations or misleading tools are a risk to patients. As payer interest in these tools increases, so does the risk that patients are harmed by the use of tools that are described as patient decision aids yet fail to meet established standards. To address this problem, the National Quality Forum (NQF) in the USA convened a multi-stakeholder expert panel in 2016 to propose national standards for a patient decision aid certification process. In 2017, NQF established an Action Team to foster shared decision making, and to call for a national certification process as one recommendation among others to stimulate improvement. A persistent barrier to the setup of a national patient decision aids certification process is the lack of a sustainable financial model to support the work.     

The potential use of decision analysis to support shared decision making in the face of uncertainty: the example of atrial fibrillation and warfarin anticoagulation

The quality of patient care is dependent upon the quality of the multitude of decisions that are made daily in clinical practice. Increasingly, modern health care is seeking to pursue better decisions (including an emphasis on evidence-based practice) and to engage patients more in decisions on their care. However, many treatment decisions are made in the face of clinical uncertainty and may be critically dependent upon patient preferences. This has led to attempts to develop decision support tools that enable patients and clinicians to make better decisions. One approach that may be of value is decision analysis, which seeks to create a rational framework for evaluating complex medical decisions and to provide a systematic way of integrating potential outcomes with probabilistic information such as that generated by randomised controlled trials of interventions. This paper describes decision analysis and discusses the potential of this approach with reference to the clinical decision as to whether to treat patients in atrial fibrillation with warfarin to reduce their risk of stroke.     

Multi-criteria decision making--an approach to setting priorities in health care

The objective of this paper is to present a multi-criteria decision making (MCDM) approach to support public health decision making that takes into consideration the fuzziness of the decision goals and the behavioural aspect of the decision maker. The approach is used to analyse the process of health technology procurement in a University Hospital in Rio de Janeiro, Brazil. The method, known as TODIM, relies on evaluating alternatives with a set of decision criteria assessed using an ordinal scale. Fuzziness in generating criteria scores and weights or conflicts caused by dealing with different viewpoints of a group of decision makers (DMs) are solved using fuzzy set aggregation rules. The results suggested that MCDM models, incorporating fuzzy set approaches, should form a set of tools for public health decision making analysis, particularly when there are polarized opinions and conflicting objectives from the DM group.     

Decision aid tools to support women's decision making in pregnancy and birth: a systematic review and meta-analysis

Support for a model of shared medical decision making, where women and their care providers discuss risks and benefits of their different options, reveal their preferences, and jointly make a decision, is a growing expectation in obstetric care. The objective of this study was to conduct a systematic review and meta-analysis of randomized controlled trials evaluating the efficacy of different decision aid tools compared to regular care for women facing several options in the specific field of obstetric care. We included published studies about interventions designed to aid mothers' decision making and provide information about obstetrical treatment or screening options. Following a search of electronic databases for articles published in English and French from 1994 to 2010, we found ten studies that met the inclusion criteria. In this systematic review and meta-analysis we found that all decision aid tools, except for Decision Trees, facilitated significant increases in knowledge. The Computer-based Information Tool, the Decision Analysis Tools, Individual Counseling and Group Counseling intervention presented significant results in reducing anxiety levels. The Decision Analysis Tools and the Computer-based Information tool were associated with a reduction in levels of decisional conflict. The Decision Analysis Tool was the only tool that presented evidence of an impact on the final choice and final outcome. Decision aid tools can assist health professionals to provide information and counseling about choices during pregnancy and support women in shared decision making. The choice of a specific tool should depend on resources available to support their use as well as the specific decisions being faced by women, their health care setting and providers.     

Improving the quality of decision-making in health care management: a project report from the Nevada/Bohemia health management education partnership.

Health care is a data-sensitive and data-rich industry. Designers of health administration curricula recognize that future health care providers and managers must be trained to use available analytical and database information technology (IT) to transform these data into information helpful to the decision-maker. However, as the data available to administrators and clinicians proliferates, additional skills are needed to select data that is pertinent and useful. This paper presents the results of the collaboration of partners from Bohemia and University of Nevada and focuses on three areas: threats to the effective use of data to support health care decision-making;. health care decision support research and training strategies; future cross-disciplinary collaboration in health care decision support. Advanced IT methods have the potential to assist clinical and managerial decision-making. If further discussion is stimulated about the introduction of advanced IT methods into the health care management curriculum and research agenda, this paper will achieve its goal.     

Toward an applied technology for quality measurement in health care

Cost containment, financial incentives to conserve resources, the growth of for-profit hospitals, an aggressive malpractice environment, and demands from purchasers are among the forces today increasing the need for improved methods that measure quality in health care. At the same time, increasingly sophisticated databases and the existence of managed care systems yield new opportunities to observe and correct quality problems. Research on targets of measurement (structure, process, and outcome) and methods of measurement (implicit, explicit, and sentinel methods) has not yet produced managerially useful applied technology for quality measurement in real-world settings. Such an applied technology would have to be cheaper, faster, more flexible, better reported, and more multidimensional than the majority of current research on quality assurance. In developing a new applied technology for the measurement of health care quality, quantitative disciplines have much to offer, such as decision support systems, criteria based on rigorous decision analyses, utility theory, tools for functional status measurement, and advances in operations research.     

Clinical decision support systems and how critical care clinicians use them

This technology overview examines the different types of clinical decision support systems and reviews how critical care physicians and registered nurses integrate these systems in their medical and nursing practices. The decision-making process is analyzed with a focus on how automated systems attempt to capture and reflect this human process in technology applications in critical care. Types of clinical decision support systems are described with an emphasis on how the varieties of systems actually organize and process information and create outcome estimations based on patient clinical indicators and prognosis logarithms. Demographic characteristics of clinicians and the implications of those demographics for patterns of decision system use, based on the body of user research, are reviewed.     

Organizing mental health services: an evidence-based approach

BACKGROUND AND AIMS: Health policy makers and program developers seek evidence-based guidance on how to organize and finance mental health services. The Swedish Council on Technology Assessment in Health Care (SBU) commissioned a conceptual framework for thinking about health care services as a medical technology. The following framework was developed, citing empirical research from mental health services research as the case example. FRAMEWORK: Historically, mental health services have focused on the organization and locus of care. Health care settings have been conceptualized as medical technologies, treatments in themselves. For example, the field speaks of an era of "asylum treatment" and "community care". Hospitals and community mental health centers are viewed as treatments with indications and "dosages", such as length of stay criteria. Assessment of mental health services often has focused on organizations and on administrative science. There are two principal perspectives for assessing the contribution of the organization of services on health. One perspective is derived from clinical services research, in which the focus is on the impact of organized treatments (and their most common settings) on health status of individuals. The other perspective is based in service systems research, in which the focus is on the impact of organizational strategies on intermediate service patterns, such as continuity of care or integration, as well as health status. METHODS: Examples of empirical investigations from clinical services research and service systems research are presented to demonstrate potential sources of evidence to support specific decisions for organizing mental health services. RESULTS: Evidence on organizing mental health services may be found in both types of services research. In clinical services research studies, service settings are viewed as treatments (e.g. "partial hospitalization"), some treatments are always embedded in a service matrix (e.g. assertive community treatment), and, where some treatments are organizationally combined (e.g. "integrated treatment" for co-occurring mental disorder and substance abuse), sometimes into a continuum of care. In service system research, integration of services and of the service system are the main focus of investigation. Studies focus on horizontal and vertical integration, primary care or specialty care and local mental health authorities - each of which may be conceptualized as a health care technology with a body of evidence assessing its effectiveness. IMPLICATIONS: A conceptual framework for assessing the organization of services as a health care technology focuses attention on evidence to guide program design and policy development. Mental health services research holds promise for such decision-making guidance.     

Primary mental health care reform: Catch the wave

There is growing energy behind primary mental health care reform – from political activity, research, funding and clinical practice. The evidence that early adopters have been busy reforming mental health services in primary health care is apparent in the rise of references to primary mental health care and collaborative care in national and provincial planning documents, clinical trials examining the efficacy of collaborative mental health care, funded clinical programs across the country, and the establishment of a nationally funded initiative – the Canadian Collaborative Mental Health Initiative – to research and develop tools to implement collaborative mental health care. In conclusion, there is real momentum in primary mental health care reform. Health care planners and executives will want to turn their attention to collaborative care as the benefits to individuals and their families, the health care system, and to the economy are clear.     

Doing mini-health technology assessments in hospitals: a new concept of decision support in health care?

OBJECTIVES: The purpose of this project was to evaluate local decision support tools used in the Danish hospital sector from a theoretical and an empirical point of view. METHODS: The use of local decision support was evaluated through questionnaires sent to all county health directors, all hospital managers, and all heads of clinical departments in cardiology, orthopedic surgery, and intensive care. In addition, respondents were asked to submit whatever decision support tools they were using (including mini-HTAs, other forms or checklists, and special procedures for decision making concerning new health technologies). A theoretical analysis of the decision support tools (decision theory) was performed as well as a comparison with the business case method used in private companies. Finally, the Danish mini-HTA was compared with foreign production and use of HTA and HTA-like assessments as local decision support. RESULTS: The response rate was high (87 percent, 94 percent, 85 percent, respectively). We collected sixty different forms (of which forty-nine were mini-HTAs) and twenty variants of written procedures. We found theoretical and empirical evidence that local involvement in the process of making the HTA could be important for the use of the results from the HTA and for the process of implementing the new technology. CONCLUSIONS: Doing mini-HTA in hospitals seems to balance the need for quality and depth with the limited time and resources for assessment.     

Dangerous omissions: the consequences of ignoring decision uncertainty

Institutions with the responsibility for making adoption (reimbursement) decisions in health care often lack the remit to demand or commission further research: adoption decisions are their only policy instrument. The decision to adopt a technology also influences the prospects of acquiring further evidence because the incentives to conduct research are reduced and the ethical basis of further clinical trials maybe undermined. In these circumstances the decision maker must consider whether the benefits of immediate access to a technology exceeds the value of the evidence which maybe forgone for future patients. We outline how these expected opportunity losses can be established from the perspective of a societal decision maker with and without the remit to commission research, and demonstrate how these considerations change the appropriate decision rules in cost‐effectiveness analysis. Importantly, we identify those circumstances in which the approval of a technology that is expected to be cost‐effective should be withheld, i.e. when an ‘only in research’ recommendation should be made. We demonstrate that a sufficient condition for immediate adoption of a technology can provide incentives for manufacturers to reduce the price or provide additional supporting evidence. However, decisions based solely on expected net benefit provide no such incentives, may undermine the evidence base for future clinical practice and reduce expected net health benefits for the patient population. Copyright © 2010 John Wiley & Sons, Ltd.     

International experiences of the active period of COVID-19 - Mental health care

AimTo summarise commonalities and variations in the mental health response to COVID-19 across different sites and countries, with a view to better understanding key steps not only in crisis management, but for future systemic reform of mental health care.MethodWe conducted a Rapid Synthesis and Translation Process of lessons learned from an international panel of experts, collecting on the ground experiences of the pandemic as it evolved in real time. Digital conferencing and individual interviews were used to rapidly acquire knowledge on the COVID-19 outbreak across 16 locations in Australia, Denmark, Italy, Spain, Taiwan, the UK, and the USA.ResultsCOVID-19 has had massive impacts on mental health care internationally. Most systems were under-resourced and under-prepared, struggling to manage both existing and new clients. There were significant differences between sites, depending on the explosivity the pandemic and the readiness of the mental health system. Integrated, community mental health systems exhibited greater adaptability in contrast to services which depended on face-to-face and hospital-based care. COVID-19 has demonstrated the need for a new approach to rapid response to crisis in mental health. New decision support system tools are necessary to ensure local decision-makers can effectively respond to the enormous practical challenges posed in these circumstances.ConclusionsThe process we have undertaken has generated clear lessons for mental health policymakers worldwide, beyond pandemic planning and response to guide next steps in systemic mental health reform. Key here is achieving some balance between national leadership and local context adaptation of evidence.     

Measuring the benefits of clinical decision support: return on investment.

In an effort to provide high quality care in a more cost-effective manner, health care providers have found it necessary to implement a series of decision support strategies designed to improve outcomes of care. While each of these strategies has measurable benefits, they each come along with additional costs. This article will describe a methodology for measuring the costs and direct and indirect benefits from decision support activities.     

Primary mental health-care model in rural Tasmania: outcomes for patients

ABSTRACT: Community and doctor concern over the disproportionately high suicide rate in a rural area of Tasmania resulted in a project employing a mental health worker to provide counselling, educate patients and the public about mental illness, improve the skills of local health workers, liaise with other counselling agencies and undertake research into mental health in the area. Figures on mental health problems in the area and the positive effects of the project on access to treatment and improvement in symptoms and functioning are presented. The results from this inexpensive, shared care/attachment model point to its potential application in primary care in other areas of Australia.     

Managing smarter: A decision support system for mental health providers

Financing of mental health care has changed radically, especially with managed care. Shrinking revenues have forced providers to look for creative ways in which to provide quality services at less expense. Delivery of quality services depends largely on the productive use of the provider's prime resource—the clinicians. Productivity was the focus of the PC-based decision support system developed for mental health providers in New York State. It enables administrators to track key indicators of productivity such as face-to-face time and non-face-to-face time against goals. Unmet goals can be pinpointed quickly, and clinicians' caseloads can be reviewed to determine the underlying causes. A key feature of the system is the conversion of raw data into actionable information to help in problem finding and problem solving. The system has been implemented in Ulster County, the pilot site for the project. The software can be customized easily to suit the data of other providers.     

Informing the development of services supporting self-care for long term mental health conditions: A mixed method study of community based mental health initiatives in England

ABSTRACT: BACKGROUND: Supporting self-care is being explored across health care systems internationally as an approach to improving care for long term conditions in the context of ageing populations and economic constraint. UK health policy advocates a range of approaches to supporting self-care, including the application of generic self-management type programmes across conditions. Within mental health, the scope of self-care remains poorly conceptualised and the existing evidence base for supporting self-care correspondingly disparate. This paper aims to inform the development of support for self-care in mental health by considering how generic approaches translate into the mental health context, and by indicating what is specific about supporting self-care for mental health. METHODS: A mixed method study was undertaken comprising standardised psychosocial measures, questionnaires about health service use and qualitative interviews with 120 new referrals to three contrasting community based initiatives supporting self-care for serious and enduring mental health conditions, repeated nine months later. A framework approach was taken to qualitative analysis, an exploratory statistical analysis sought to identify possible associations between a range of independent variables and self-care outcomes, and a narrative synthesis brought these analyses together. RESULTS: Participants reported improvement in self-care outcomes (e.g. empowerment; less use of Accident and Emergency services). These changes were not associated with level of engagement with self-care support. Level of engagement was associated with positive collaboration with support staff. Qualitative data described the value of different models of supporting self-care and considered challenges. Synthesis of analyses suggested that timing support for self-care, giving service users control over when and how they accessed support, quality of service user-staff relationships and decision making around medication are important issues in supporting self-care in mental health. CONCLUSIONS: Generic elements of support for self-care - e.g. peer support groups, personal planning - have value when translated into a mental health context. Support for self-care in mental health should focus on core, mental health specific qualities - issues of control, enabling staff-service user relationships and shared decision making - rather than simply the provision of standardised service components. The broad empirical basis of our research indicates the wider relevance of our findings across mental health settings.