Current models of healthcare quality recommend that patient management decisions be evidence-based and patient-centered. Evidence-based decisions require a thorough understanding of current information regarding the natural history of disease and the anticipated outcomes of different management options. Patient-centered decisions incorporate patient preferences, values, and unique personal circumstances into the decision making process and actively involve both patients along with health care providers as much as possible. Fundamentally, therefore, evidence-based, patient-centered decisions are multi-dimensional and typically involve multiple decision makers.
Advances in the decision sciences have led to the development of a number of multiple criteria decision making methods. These multi-criteria methods are designed to help people make better choices when faced with complex decisions involving several dimensions. They are especially helpful when there is a need to combine “hard data” with subjective preferences, to make trade-offs between desired outcomes, and to involve multiple decision makers. Evidence-based, patient-centered clinical decision making has all of these characteristics. This close match suggests that clinical decision support systems based on multi-criteria decision making techniques have the potential to enable patients and providers to carry out the tasks required to implement evidence-based, patient-centered care effectively and efficiently in clinical settings.
The goal of this paper is to give readers a general introduction to the range of multi-criteria methods available and show how they could be used to support clinical decision-making. Methods discussed include the balance sheet, the even swap method, ordinal ranking methods, direct weighting methods, multi-attribute decision analysis, and the analytic hierarchy process (AHP)
Decisions about the use of new technologies in health care are often based on complex economic models. Decision makers frequently make informal judgments about evidence, uncertainty, and the assumptions that underpin these models.
Transparent interactive decision interrogator (TIDI) facilitates more formal critique of decision models by decision makers such as members of appraisal committees of the National Institute for Health and Clinical Excellence in the UK. By allowing them to run advanced statistical models under different scenarios in real time, TIDI can make the decision process more efficient and transparent, while avoiding limitations on pre-prepared analysis.
TIDI, programmed in Visual Basic for applications within Excel, provides an interface for controlling all components of a decision model developed in the appropriate software (e.g., meta-analysis in WinBUGS and the decision model in R) by linking software packages using RExcel and R2WinBUGS. TIDI's graphical controls allow the user to modify assumptions and to run the decision model, and results are returned to an Excel spreadsheet. A tool displaying tornado plots helps to evaluate the influence of individual parameters on the model outcomes, and an interactive meta-analysis module allows the user to select any combination of available studies, explore the impact of bias adjustment, and view results using forest plots. We demonstrate TIDI using an example of a decision model in antenatal care.
Use of TIDI during the NICE appraisal of tumor necrosis factor-alpha inhibitors (in psoriatic arthritis) successfully demonstrated its ability to facilitate critiques of the decision models by decision makers.
bias adjustment; decision model; interactive; meta-analysis; RExcel; software; TIDI
Current healthcare systems have extended the evidence-based medicine (EBM) approach to health policy and delivery decisions, such as access-to-care, healthcare funding and health program continuance, through attempts to integrate valid and reliable evidence into the decision making process. These policy decisions have major impacts on society and have high personal and financial costs associated with those decisions. Decision models such as these function under a shared assumption of rational choice and utility maximization in the decision-making process.
We contend that health policy decision makers are generally unable to attain the basic goals of evidence-based decision making (EBDM) and evidence-based policy making (EBPM) because humans make decisions with their naturally limited, faulty, and biased decision-making processes. A cognitive information processing framework is presented to support this argument, and subtle cognitive processing mechanisms are introduced to support the focal thesis: health policy makers' decisions are influenced by the subjective manner in which they individually process decision-relevant information rather than on the objective merits of the evidence alone. As such, subsequent health policy decisions do not necessarily achieve the goals of evidence-based policy making, such as maximizing health outcomes for society based on valid and reliable research evidence.
In this era of increasing adoption of evidence-based healthcare models, the rational choice, utility maximizing assumptions in EBDM and EBPM, must be critically evaluated to ensure effective and high-quality health policy decisions. The cognitive information processing framework presented here will aid health policy decision makers by identifying how their decisions might be subtly influenced by non-rational factors. In this paper, we identify some of the biases and potential intervention points and provide some initial suggestions about how the EBDM/EBPM process can be improved.
Substitute decision-makers are integral to the care of dying patients and make many healthcare decisions for patients. Unfortunately, conflict between physicians and surrogate decision-makers is not uncommon in end-of-life care and this could contribute to a “bad death” experience for the patient and family. We aim to describe Canadian family physicians’ experiences of conflict with substitute decision-makers of dying patients to identify factors that may facilitate or hinder the end-of-life decision-making process. This insight will help determine how to best manage these complex situations, ultimately improving the overall care of dying patients.
Grounded Theory methodology was used with semi-structured interviews of family physicians in Edmonton, Canada, who experienced conflict with substitute decision-makers of dying patients. Purposeful sampling included maximum variation and theoretical sampling strategies. Interviews were audio-taped, and transcribed verbatim. Transcripts, field notes and memos were coded using the constant-comparative method to identify key concepts until saturation was achieved and a theoretical framework emerged.
Eleven family physicians with a range of 3 to 40 years in clinical practice participated.
The family physicians expressed a desire to achieve a “good death” and described their role in positively influencing the experience of death.
Finding Common Ground to Achieve a “Good Death” for the Patient emerged as an important process which includes 1) Building Mutual Trust and Rapport through identifying key players and delivering manageable amounts of information, 2) Understanding One Another through active listening and ultimately, and 3) Making Informed, Shared Decisions. Facilitators and barriers to achieving Common Ground were identified. Barriers were linked to conflict. The inability to resolve an overt conflict may lead to an impasse at any point. A process for Resolving an Impasse is described.
A novel framework for developing Common Ground to manage conflicts during end-of-life decision-making discussions may assist in achieving a “good death”. These results could aid in educating physicians, learners, and the public on how to achieve productive collaborative relationships during end-of-life decision-making for dying patients, and ultimately improve their deaths.
Family medicine; Advance care planning; Conflict; Substitute decision-makers; Good death
To illuminate and synthesize what is known about the underlying decision making processes surrounding couples’ preimplantation genetic diagnosis (PGD) use or disuse and to formulate an initial conceptual framework that can guide future research and practice.
This systematic review targeted empirical studies published in English from 1990 to 2008 that examined the decision making process of couples or individual partners that had used, were eligible for, or had contemplated PGD. Sixteen studies met the eligibility requirements. To provide a more comprehensive review, empirical studies that examined healthcare professionals’ perceptions of couples’ decision making surrounding PGD use and key publications from a variety of disciplines supplemented the analysis.
The conceptual framework formulated from the review demonstrates that couples’ PGD decision making is composed of three iterative and dynamic dimensions: cognitive appraisals, emotional responses, and moral judgments.
Couples think critically about uncertain and probabilistic information, grapple with conflicting emotions and incorporate moral perspectives into their decision making about whether or not to use PGD.
The quality of care and decisional support for couples who are contemplating PGD use can be improved by incorporating focused questions and discussion from each of the dimensions into counseling sessions.
Decision making in clinical practice often involves the need to make complex and intricate decisions with important long-term consequences. Decision analysis is a tool that allows users to apply evidence-based medicine to make informed and objective clinical decisions when faced with complex situations. A Decision Tree, together with literature-derived probabilities and defined outcome values, is used to model a given problem and help determine the best course of action. Sensitivity analysis allows an exploration of important variables on final clinical outcomes. A decision-maker can thereafter establish a preferred method of treatment and explore variables which influence the final outcome. The present paper is intended to give an overview of decision analysis and its application in clinical decision making.
Clinical trials; critical appraisal; decision analysis; evidence-based medicine; hierarchy of evidence
Decision aids have been developed in a number of health disciplines to support evidence-informed decision making, including patient decision aids and clinical practice guidelines. However, policy contexts differ from clinical contexts in terms of complexity and uncertainty, requiring different approaches for identifying, interpreting, and applying many different types of evidence to support decisions. With few studies in the literature offering decision guidance specifically to health policymakers, the present study aims to facilitate the structured and systematic incorporation of research evidence and, where there is currently very little guidance, values and other non-research-based evidence, into the policy making process. The resulting decision aid is intended to help public sector health policy decision makers who are tasked with making evidence-informed decisions on behalf of populations. The intent is not to develop a decision aid that will yield uniform recommendations across jurisdictions, but rather to facilitate more transparent policy decisions that reflect a balanced consideration of all relevant factors.
The study comprises three phases: a modified meta-narrative review, the use of focus groups, and the application of a Delphi method. The modified meta-narrative review will inform the initial development of the decision aid by identifying as many policy decision factors as possible and other features of methodological guidance deemed to be desirable in the literatures of all relevant disciplines. The first of two focus groups will then seek to marry these findings with focus group members' own experience and expertise in public sector population-based health policy making and screening decisions. The second focus group will examine issues surrounding the application of the decision aid and act as a sounding board for initial feedback and refinement of the draft decision aid. Finally, the Delphi method will be used to further inform and refine the decision aid with a larger audience of potential end-users.
The product of this research will be a working version of a decision aid to support policy makers in population-based health policy decisions. The decision aid will address the need for more structured and systematic ways of incorporating various evidentiary sources where applicable.
Hospitalized older adults frequently have impaired cognition and must rely on surrogates to make major medical decisions. Ethical standards for surrogate decision making are well delineated, but little is known about what factors surrogates actually consider when making decisions.
To determine factors surrogate decision makers consider when making major medical decisions for hospitalized older adults, and whether or not they adhere to established ethical standards.
Semi-structured interview study of the experience and process of decision making.
A public safety-net hospital and a tertiary referral hospital in a large city in the Midwest.
Thirty-five surrogates with a recent decision making experience for an inpatient age 65 and older.
Key factors surrogates considered when making decisions. Interview transcripts were coded and analyzed using the grounded theory method of qualitative analysis.
Surrogates considered patient-centered factors and surrogate-centered factors. Patient-centered factors included: 1) respecting the patient’s input, (2) using past knowledge of patient to infer the patient’s wishes, and (3) considering what is in the patient’s best interests. Some surrogates expressed a desire for more information about the patient’s prior wishes. Surrogate-centered factors included 1) Surrogate’s wishes as a guide, (2) The surrogate’s religious beliefs and/or spirituality, (3) The surrogate’s interests, (4) Family consensus and (5) Obligation and guilt.
These data show that surrogate decision making is more complex than the standard ethical models, which are limited to patient autonomy and beneficence. Because surrogates also imagine what they would want under the circumstances and consider their own needs and preferences, models of surrogate decision making must account for these additional considerations. Surrogates’ desire for more information about patient preferences suggests a need for greater advance care planning.
New products evolving from research and development can only be translated to medical practice on a large scale if they are reimbursed by third-party payers. Yet the decision processes regarding reimbursement are highly complex and internationally heterogeneous. This study develops a process-oriented framework for monitoring these so-called fourth hurdle procedures in the context of product development from bench to bedside. The framework is suitable both for new drugs and other medical technologies.
The study is based on expert interviews and literature searches, as well as an analysis of 47 websites of coverage decision-makers in England, Germany and the USA.
Eight key steps for monitoring fourth hurdle procedures from a company perspective were determined: entering the scope of a healthcare payer; trigger of decision process; assessment; appraisal; setting level of reimbursement; establishing rules for service provision; formal and informal participation; and publication of the decision and supplementary information. Details are given for the English National Institute for Health and Clinical Excellence, the German Federal Joint Committee, Medicare's National and Local Coverage Determinations, and for Blue Cross Blue Shield companies.
Coverage determination decisions for new procedures tend to be less formalized than for novel drugs. The analysis of coverage procedures and requirements shows that the proof of patient benefit is essential. Cost-effectiveness is likely to gain importance in future.
Continued improvements in occupational health can only be ensured if decisions regarding the implementation and continuation of occupational health and safety interventions (OHS interventions) are based on the best available evidence. To ensure that this is the case, scientific evidence should meet the needs of decision-makers. As a first step in bridging the gap between the economic evaluation literature and daily practice in occupational health, this study aimed to provide insight into the occupational health decision-making process and information needs of decision-makers.
An exploratory qualitative study was conducted with a purposeful sample of occupational health decision-makers in the Ontario healthcare sector. Eighteen in-depth interviews were conducted to explore the process by which occupational health decisions are made and the importance given to the financial implications of OHS interventions. Twenty-five structured telephone interviews were conducted to explore the sources of information used during the decision-making process, and decision-makers’ knowledge on economic evaluation methods. In-depth interview data were analyzed according to the constant comparative method. For the structured telephone interviews, summary statistics were prepared.
The occupational health decision-making process generally consists of three stages: initiation stage, establishing the need for an intervention; pre-implementation stage, developing an intervention and its business case in order to receive senior management approval; and implementation and evaluation stage, implementing and evaluating an intervention. During this process, information on the financial implications of OHS interventions was found to be of great importance, especially the employer’s costs and benefits. However, scientific evidence was rarely consulted, sound ex-post program evaluations were hardly ever performed, and there seemed to be a need to advance the economic evaluation skill set of decision-makers.
Financial information is particularly important at the front end of implementation decisions, and can be a key deciding factor of whether to go forward with a new OHS intervention. In addition, it appears that current practice in occupational health in the healthcare sector is not solidly grounded in evidence-based decision-making and strategies should be developed to improve this.
Occupational health and safety; Interventions; Decision-making process; Information needs; Evidence-based practice
Systematic reviews, which were developed to improve policy-making and clinical decision-making, answer an empirical question based on a minimally biased appraisal of all the relevant empirical studies. A model is presented here for writing systematic reviews of argument-based literature: literature that uses arguments to address conceptual questions, such as whether abortion is morally permissible or whether research participants should be legally entitled to compensation for sustaining research-related injury. Such reviews aim to improve ethically relevant decisions in healthcare, research or policy. They are better tools than informal reviews or samples of literature with respect to the identification of the reasons relevant to a conceptual question, and they enable the setting of agendas for conceptual and empirical research necessary for sound policy-making. This model comprises prescriptions for writing the systematic review's review question and eligibility criteria, the identification of the relevant literature, the type of data to extract on reasons and publications, and the derivation and presentation of results. This paper explains how to adapt the model to the review question, literature reviewed and intended readers, who may be decision-makers or academics. Obstacles to the model's application are described and addressed, and limitations of the model are identified.
Bioethics; decision making; ethics and evidence-based medicine (EBM); guideline development; health policy; information ethics; methods in empirical bioethics; review literature as topic; systematic review; technology/risk assessment
A survey of black and white family physicians in the District of Columbia is described. The survey provides insight into decision-making processes and the ability to recognize ethical dilemmas in medical practice. Comments were elicited to hypothetical case vignettes typical of ethical conflict in office practice. Findings note physician ability to recognize ethical dilemmas in day-to-day aspects of medical practice. Methods of decision making and rationale for decisions made, however, appear to be inconsistent, nonuniversal, and individualistic without evidence of specific models or criteria. No significant differences were noted between black and white physicians. The need in physician training for clarification and development of criteria is evident.
Despite the complexity and variability of decision processes, motor responses are generally stereotypical and independent of decision difficulty. How is this consistency achieved? Through an engineering analogy we consider how and why a system should be designed to realise not only flexible decision-making, but also consistent decision implementation. We specifically consider neurobiologically-plausible accumulator models of decision-making, in which decisions are made when a decision threshold is reached. To trade-off between the speed and accuracy of the decision in these models, one can either adjust the thresholds themselves or, equivalently, fix the thresholds and adjust baseline activation. Here we review how this equivalence can be implemented in such models. We then argue that manipulating baseline activation is preferable as it realises consistent decision implementation by ensuring consistency of motor inputs, summarise empirical evidence in support of this hypothesis, and suggest that it could be a general principle of decision making and implementation. Our goal is therefore to review how neurobiologically-plausible models of decision-making can manipulate speed-accuracy trade-offs using different mechanisms, to consider which of these mechanisms has more desirable decision-implementation properties, and then review the relevant neuroscientific data on which mechanism brains actually use.
Shared decision making contributes to high quality healthcare by promoting a patient-centered approach. Patient involvement in selecting the components of a diabetes medication program that best match the patient’s values and preferences may also enhance medication adherence and improve outcomes. Decision aids are tools designed to involve patients in shared decision making, but their adoption in practice has been limited. In this study, we propose to obtain a preliminary estimate of the impact of patient decision aids vs. usual care on measures of patient involvement in decision making, diabetes care processes, medication adherence, glycemic and cardiovascular risk factor control, and resource utilization. In addition, we propose to identify, describe, and explain factors that promote or inhibit the routine embedding of decision aids in practice.
We will be conducting a mixed-methods study comprised of a cluster-randomized, practical, multicentered trial enrolling clinicians and their patients (n = 240) with type 2 diabetes from rural and suburban primary care practices (n = 8), with an embedded qualitative study to examine factors that influence the incorporation of decision aids into routine practice. The intervention will consist of the use of a decision aid (Statin Choice and Aspirin Choice, or Diabetes Medication Choice) during the clinical encounter. The qualitative study will include analysis of video recordings of clinical encounters and in-depth, semi-structured interviews with participating patients, clinicians, and clinic support staff, in both trial arms.
Upon completion of this trial, we will have new knowledge about the effectiveness of diabetes decision aids in these practices. We will also better understand the factors that promote or inhibit the successful implementation and normalization of medication choice decision aids in the care of chronic patients in primary care practices.
Diabetes; Shared decision making; Cardiovascular prevention; Implementation
The overall goal of our research agenda is to contribute to improved quality of healthcare by identifying factors that foster or inhibit the use of healthcare information by patients to make informed healthcare decisions. We propose to study the natural history of the use of healthcare information by women with breast cancer to support decisions about health care. To do so in this paper we propose a conceptual model developed based on an extensive literature review and critique that describes patients' health information use over the disease course. It will guide our further investigation of the complex relationships among patients' personal circumstances, the progress of their medical treatment, and their satisfaction and empowerment as informed decision-makers. The model will help policy makers and health professionals identify the best means to provide patients with useful information, and help all stakeholders in health care acquire information needed to improve healthcare quality.
Healthcare decisionmaking is a complex process relying on disparate types of evidence and value judgments. Our objectives for this study were to develop a practical framework to facilitate decisionmaking in terms of supporting the deliberative process, providing access to evidence, and enhancing the communication of decisions.
Extensive analyses of the literature and of documented decisionmaking processes around the globe were performed to explore what steps are currently used to make decisions with respect to context (from evidence generation to communication of decision) and thought process (conceptual components of decisions). Needs and methodologies available to support decisionmaking were identified to lay the groundwork for the EVIDEM framework.
A framework was developed consisting of seven modules that can evolve over the life cycle of a healthcare intervention. Components of decision that could be quantified, i.e., intrinsic value of a healthcare intervention and quality of evidence available, were organized into matrices. A multicriteria decision analysis (MCDA) Value Matrix (VM) was developed to include the 15 quantifiable components that are currently considered in decisionmaking. A methodology to synthesize the evidence needed for each component of the VM was developed including electronic access to full text source documents. A Quality Matrix was designed to quantify three criteria of quality for the 12 types of evidence usually required by decisionmakers. An integrated system was developed to optimize data analysis, synthesis and validation by experts, compatible with a collaborative structure.
The EVIDEM framework promotes transparent and efficient healthcare decisionmaking through systematic assessment and dissemination of the evidence and values on which decisions are based. It provides a collaborative framework that could connect all stakeholders and serve the healthcare community at local, national and international levels by allowing sharing of data, resources and values. Validation and further development is needed to explore the full potential of this approach.
Computerized clinical decision support systems are information technology-based systems designed to improve clinical decision-making. As with any healthcare intervention with claims to improve process of care or patient outcomes, decision support systems should be rigorously evaluated before widespread dissemination into clinical practice. Engaging healthcare providers and managers in the review process may facilitate knowledge translation and uptake. The objective of this research was to form a partnership of healthcare providers, managers, and researchers to review randomized controlled trials assessing the effects of computerized decision support for six clinical application areas: primary preventive care, therapeutic drug monitoring and dosing, drug prescribing, chronic disease management, diagnostic test ordering and interpretation, and acute care management; and to identify study characteristics that predict benefit.
The review was undertaken by the Health Information Research Unit, McMaster University, in partnership with Hamilton Health Sciences, the Hamilton, Niagara, Haldimand, and Brant Local Health Integration Network, and pertinent healthcare service teams. Following agreement on information needs and interests with decision-makers, our earlier systematic review was updated by searching Medline, EMBASE, EBM Review databases, and Inspec, and reviewing reference lists through 6 January 2010. Data extraction items were expanded according to input from decision-makers. Authors of primary studies were contacted to confirm data and to provide additional information. Eligible trials were organized according to clinical area of application. We included randomized controlled trials that evaluated the effect on practitioner performance or patient outcomes of patient care provided with a computerized clinical decision support system compared with patient care without such a system.
Data will be summarized using descriptive summary measures, including proportions for categorical variables and means for continuous variables. Univariable and multivariable logistic regression models will be used to investigate associations between outcomes of interest and study specific covariates. When reporting results from individual studies, we will cite the measures of association and p-values reported in the studies. If appropriate for groups of studies with similar features, we will conduct meta-analyses.
A decision-maker-researcher partnership provides a model for systematic reviews that may foster knowledge translation and uptake.
The concept of evidence-based medicine has strongly influenced the appraisal and application of empirical information in health care decision-making. One principal characteristic of this concept is the distinction between "evidence" in the sense of high-quality empirical information on the one hand and rather low-quality empirical information on the other hand. In the last 5 to 10 years an increasing number of articles published in international journals have made use of the term "evidence-based ethics", making a systematic analysis and explication of the term and its applicability in ethics important.
In this article four descriptive and two normative characteristics of the general concept "evidence-based" are presented and explained systematically. These characteristics are to then serve as a framework for assessing the methodological and practical challenges of evidence-based ethics as a developing methodology. The superiority of evidence in contrast to other empirical information has several normative implications such as the legitimization of decisions in medicine and ethics. This implicit normativity poses ethical concerns if there is no formal consent on which sort of empirical information deserves the label "evidence" and which does not. In empirical ethics, which relies primarily on interview research and other methods from the social sciences, we still lack gold standards for assessing the quality of study designs and appraising their findings.
The use of the term "evidence-based ethics" should be discouraged, unless there is enough consensus on how to differentiate between high- and low-quality information produced by empirical ethics. In the meantime, whenever empirical information plays a role, the process of ethical decision-making should make use of systematic reviews of empirical studies that involve a critical appraisal and comparative discussion of data.
Accreditation in France relies on a mandatory 4-year cycle of self-assessment and a peer review of 82 standards, among which 14 focus priority standards (FPS). Hospitals are also required to measure yearly quality indicators (QIs—5 in 2010). On advice given by the accreditation committee of HAS (Haute Autorité en Santé), based on surveyors proposals and relying mostly on compliance to standards, accreditation decisions are taken by the board of HAS. Accreditation is still perceived by hospitals as a burdensome process and a simplification would be welcomed. The hypothesis was that a more limited number of criteria might give sufficient amount of information on hospitals overall quality level, appraised today by accreditation decisions.
The accuracy of predictions of accreditation decisions given by a model, Partial Least Square-2 Discriminant Analysis (PLS2-DA), using only the results of FPS and QIs was measured. Accreditation decisions (full accreditation (A), recommendations or reservation (B), remit decision or non-accreditation (C)), results of FPS and QIs were considered qualitative variables. Stability was assessed by leave one out cross validation (LOOCV).
Setting and participants
All French 489 acute care organisations (ACO) accredited between June 2010 and January 2012 were considered, 304 of them having a rehabilitation care sector (RCS).
Accuracy of prediction of accreditation decisions was good (89% of ACOs and 91% of ACO-RCS well classified). Stability of results appeared satisfactory when using LOOCV (87% of ACOs and 89% of ACO-RCS well classified). Identification of worse hospitals was correct (90% of ACOs and 97% of ACO-RCS predicted C were actually C).
Using PLS2-DA with a limited number of criteria (QIs and FPS) provides an accurate prediction of accreditation decisions, especially for underperforming hospitals. This could support accreditation committees which give advices on accreditation decisions, and allow fast-track handling of ‘safe’ reports.
Childbirth is one of the most painful events that a woman is likely to experience, the multi-dimensional aspect and intensity of which far exceeds disease conditions. A woman's lack of knowledge about the risks and benefits of the various methods of pain relief can heighten anxiety. Women are increasingly expected, and are expecting, to participate in decisions about their healthcare. Involvement should allow women to make better-informed decisions; the National Institute for Clinical Excellence has stated that we need effective ways of supporting pregnant women in making informed decisions during labour. Our aim was to systematically review the empirical literature on women's expectations and experiences of pain and pain relief during labour, as well as their involvement in the decision-making process.
A systematic review was conducted using the following databases: Medical Literature Analysis and Retrieval System Online (MEDLINE), Cumulative Index to Nursing and Allied Health Literature (CINAHL), Bath Information and Database Service (BIDS), Excerpta Medica Database Guide (EMBASE), Midwives Information and Resource (MIDIRS), Sociological Abstracts and PsychINFO. Studies that examined experience and expectations of pain, and its relief in labour, were appraised and the findings were integrated into a systematic review.
Appraisal revealed four key themes: the level and type of pain, pain relief, involvement in decision-making and control. Studies predominantly showed that women underestimated the pain they would experience. Women may hope for a labour free of pain relief, but many found that they needed or benefited from it. There is a distinction between women's desire for a drug-free labour and the expectation that they may need some sort of pain relief. Inaccurate or unrealistic expectations about pain may mean that women are not prepared appropriately for labour. Many women acknowledged that they wanted to participate in decision-making, but the degree of involvement varied. Women expected to take control in labour in a number of ways, but their degree of reported control was less than hoped for.
Women may have ideal hopes of what they would like to happen with respect to pain relief, control and engagement in decision-making, but experience is often very different from expectations. Antenatal educators need to ensure that pregnant women are appropriately prepared for what might actually happen to limit this expectation-experience gap and potentially support greater satisfaction with labour.
Health decision making is both the lynchpin and the least developed aspect of evidence-based practice. The evidence-based practice process requires integrating the evidence with consideration of practical resources and patient preferences and doing so via a process that is genuinely collaborative. Yet, the literature is largely silent about how to accomplish integrative, shared decision making. Implications for evidence-based practice are discussed for 2 theories of clinician decision making (expected utility and fuzzy trace) and 2 theories of patient health decision making (transtheoretical model and reasoned action). Three suggestions are offered. First, it would be advantageous to have theory-based algorithms that weight and integrate the 3 data strands (evidence, resources, preferences) in different decisional contexts. Second, patients, not providers, make the decisions of greatest impact on public health, and those decisions are behavioral. Consequently, theory explicating how provider-patient collaboration can influence patient lifestyle decisions made miles from the provider's office is greatly needed. Third, although the preponderance of data on complex decisions supports a computational approach, such an approach to evidence-based practice is too impractical to be widely applied at present. More troublesomely, until patients come to trust decisions made computationally more than they trust their providers’ intuitions, patient adherence will remain problematic. A good theory of integrative, collaborative health decision making remains needed.
decision making; decision theory; evidence-based practice; evidence-based medicine; clinical competence; practice guidelines clinical psychology
Healthcare decision-making can be complex, often requiring decision makers to weigh serious trade-offs, consider patients’ values, and incorporate evidence in the face of uncertainty. Medical decisions are made implicitly by clinicians and other decision-makers on a daily basis. Decisions based largely on personal experience are subject to many biases. Decision analysis and cost-effectiveness analysis are systematic approaches used to support decision-making under conditions of uncertainty that involve important trade-offs. These mathematical tools can provide patients, physicians and policy makers with a useful approach to complex medical decision making.
Assisting patients and their families in complex decision making is a foundational skill in palliative care; however, palliative care clinicians and scientists have just begun to establish an evidence base for best practice in assisting patients and families in complex decision making. Decision scientists aim to understand and clarify the concepts and techniques of shared decision making (SDM), decision support, and informed patient choice in order to ensure that patient and family perspectives shape their health care experience. Patients with serious illness and their families are faced with myriad complex decisions over the course of illness and as death approaches. If patients lose capacity, then surrogate decision makers are cast into the decision-making role. The fields of palliative care and decision science have grown in parallel. There is much to be gained in advancing the practices of complex decision making in serious illness through increased collaboration. The purpose of this article is to use a case study to highlight the broad range of difficult decisions, issues, and opportunities imposed by a life-limiting illness in order to illustrate how collaboration and a joint research agenda between palliative care and decision science researchers, theorists, and clinicians might guide best practices for patients and their families.
Partially Observable Markov Decision Processes have been studied widely as a model for decision making under uncertainty, and a number of methods have been developed to find the solutions for such processes. Such studies often involve calculation of the value function of a specific policy, given a model of the transition and observation probabilities, and the reward. These models can be learned using labeled samples of on-policy trajectories. However, when using empirical models, some bias and variance terms are introduced into the value function as a result of imperfect models. In this paper, we propose a method for estimating the bias and variance of the value function in terms of the statistics of the empirical transition and observation model. Such error terms can be used to meaningfully compare the value of different policies. This is an important result for sequential decision-making, since it will allow us to provide more formal guarantees about the quality of the policies we implement. To evaluate the precision of the proposed method, we provide supporting experiments on problems from the field of robotics and medical decision making.
Computer disease simulation models are increasingly being used to evaluate and inform healthcare decisions across medical disciplines. The aim of researchers who develop these models is to integrate and synthesize short-term outcomes and results from multiple sources to predict the long-term clinical outcomes and costs of different healthcare strategies. Policy makers, in turn, can use the predictions generated by disease models together with other evidence to make decisions related to healthcare practices and resource utilization. Models are particularly useful when the existing evidence does not yield obvious answers or does not provide answers to the questions of greatest interest, such as questions about the relative cost effectiveness of different practices. In this review we focus on models used to inform decisions about imaging technology, discussing the role of disease models for health policy development and providing a foundation for understanding the basic principles of disease modeling. We draw from the collective computed tomographic colonography (CTC) modeling experience, reviewing 10 published investigations of the clinical effectiveness and cost-effectiveness of CTC relative to colonoscopy. We discuss the implications of different modeling assumptions and difficulties that may be encountered when evaluating the quality of models. Finally, we underscore the importance of forging stronger collaborations between researchers who develop disease models and radiologists, in order to ensure that policy-level models accurately represent the experience of everyday clinical practices.
Medical Imaging; Computer Simulation; Colonography; Computed Tomographic