Risk communication in a patient decision aid for radiotherapy in breast cancer: How to deal with uncertainty?

Background and aimPatient decision aids for oncological treatment options, provide information on the effect on recurrence rates and/or survival benefit, and on side-effects and/or burden of different treatment options. However, often uncertainty exists around the probability estimates for recurrence/survival and side-effects which is too relevant to be ignored. Evidence is lacking on the best way to communicate these uncertainties. The aim of this study is to develop a method to incorporate uncertainties in a patient decision aid for breast cancer patients to support their decision on radiotherapy.MethodsFirstly, qualitative interviews were held with patients and health care professionals. Secondly, in the development phase, thinking aloud sessions were organized with four patients and 12 health care professionals, individual and group-wise.ResultsConsensus was reached on a pictograph illustrating the whole range of uncertainty for local recurrence risks, in combination with textual explanation that a more exact personalized risk would be given by their own physician. The pictograph consisted of 100 female icons in a 10 x 10 array. Icons with a stepwise gradient color indicated the uncertainty margin. The prevalence and severity of possible side-effects were explained using verbal labels.ConclusionsWe developed a novel way of visualizing uncertainties in recurrence rates in a patient decision aid. The effect of this way of communicating risk uncertainty is currently being tested in the BRASA study (NCT03375801).     

Commentary: At the Center of Decision Making in Mental Health Services and Interventions Research: Patients,Clinicians, or Relationships?

Wills and Holmes-Rovner (2006) highlight the fact that despite growing interest in the role of patient preferences and shared decision making with clinicians in the general health services research community, relatively little is known about the impact of these preferences and processes on actual decisions, service delivery engagement, or intervention outcomes in the mental health field. This commentary expands on three important points raised in Wills and Holmes-Rovner's article: (a) the need for more and better research on values assessment, (b) contextual factors in the decision-making process, and (c) the measurement of patient preferences regarding their level of involvement in decision making.     

Shared decision making in consultations for hypertension: Qualitative study in general practice

BackgroundHypertension is mainly managed in primary care. Shared decision making is widely recommended as an approach to treatment decision making. However, no studies have investigated; in detail, what happens during primary care consultations for hypertension.AimTo understand patients’ and clinicians’ experience of shared decision making for hypertension in primary care, in order to propose how it might be better supported.DesignLongitudinal qualitative study.SettingFive general practices in south‐west England.MethodInterviews with a purposive sample of patients with hypertension, and with the health‐care practitioners they consulted, along with observations of clinical consultations, for up to 6 appointments. Interviews and consultations were audio‐recorded and observational field notes taken. Data were analysed thematically.ResultsForty‐six interviews and 18 consultations were observed, with 11 patients and nine health‐care practitioners (five GPs, one pharmacist and three nurses). Little shared decision making was described by participants or observed. Often patients’ understanding of their hypertension was limited, and they were not aware there were treatment choices. Consultations provided few opportunities for patients and clinicians to reach a shared understanding of their treatment choices. Opportunities for patients to engage in choices were limited by structured consultations and the distribution of decisions across consultations.ConclusionFor shared decision making to be better supported, consultations need to provide opportunities for patients to learn about their condition, to understand that there are treatment choices, and to discuss these choices with clinicians.Patient or Public ContributionA patient group contributed to the design of this study.     

Can Machine Learning Methods Produce Accurate and Easy-to-Use Preoperative Prediction Models of One-Year Improvements in Pain and Functioning After Knee Arthroplasty?

BackgroundApproximately 15%-20% of total knee arthroplasty (TKA) patients do not experience clinically meaningful improvements. We sought to compare the accuracy and parsimony of several machine learning strategies for developing predictive models of failing to experience minimal clinically important differences in patient-reported outcome measures (PROMs) 1 year after TKA.MethodsPatients (N = 587) in 3 large Veteran Health Administration facilities completed PROMs before and 1 year after TKA (92% follow-up). Preoperative PROMs and electronic health record data were used to develop and validate models to predict failing to experience at least a minimal clinically important difference in Knee Injury and Osteoarthritis Outcome Score (KOOS) Total, KOOS JR, and KOOS subscales (Pain, Symptoms, Activities of Daily Living, Quality of Life, and recreation). Several machine learning strategies were used for model development. Ten-fold cross-validation and bootstrapping were used to produce measures of overall accuracy (C-statistic, Brier Score). The sensitivity and specificity of various predicted probability cut-points were examined.ResultsThe most accurate models produced were for the Activities of Daily Living, Pain, Symptoms, and Quality of Life subscales of the KOOS (C-statistics 0.76, 0.72, 0.72, and 0.71, respectively). Strategies varied substantially in terms of the numbers of inputs required to achieve similar accuracy, with none being superior for all outcomes.ConclusionModels produced in this project provide estimates of patient-specific improvements in major outcomes 1 year after TKA. Integrating these models into clinical decision support, informed consent and shared decision making could improve patient selection, education, and satisfaction.Level of EvidenceLevel III, diagnostic study.     

关于"惠民"医疗服务的实践与思考

惠民医疗是指对民政部门认定核准,缺乏经济支持的最低生活保障待遇家庭中没有享受政府、社会、单位医疗保障待遇的,并确有病需治疗的人员实行五免五减半医疗服务。南京市儿童医院针对惠民医疗服务中问题,对惠民医疗服务界定、服务模式、补偿机制和服务远景做出了思考。     

Surgical management of pelvic organ prolapse

Pelvic organ prolapse is a common condition which can have a profound effect on health-related quality of life. The lifetime risk of surgery for pelvic organ prolapse for all women is around 10–12%, making prolapse surgery one of the most commonly performed operations of all. Surgery is generally offered only to women to do not respond to conservative measures such as physiotherapy and/or vaginal pessaries. It is very important to ensure excellent clinical governance around the decision-making process for this elective surgical problem, and this may include the use of written information, face to face and telephone consultations, patient reported outcome measures and patient decision aids. This chapter will cover all the different techniques for prolapse surgery including conventional approaches using native tissue, uterus conserving prolapse surgery and surgery for post-hysterectomy vault prolapse. This will also include laparoscopic prolapse surgery. The role of mesh in prolapse surgery will also be discussed and this chapter will cover important topics including patient selection and preparation for prolapse surgery, shared decision making and how best to facilitate this, patient preparation before prolapse surgery and follow-up post operation.     

Contemporary Health-Related Decision Aids: Tools for Social Work Practice

The escalating complexity in health-related decisions that people face have important implications for social work interventions. This article explores the nature of these implications within the context of decisional conflict, shared decision making, and the use of decision aids. In addition, the authors present the findings of a content analysis of 29 contemporary health-related decision aids. Emergent categories from this analysis are presented as a resource for social workers as they encounter, adapt, and create decision aids in their work to help address the health-related needs of their clients.