Measuring performance in healthcare

Hospitals invest in process management and process optimization from an organizational and patient perspective to increase efficiency and simultaneously the quality of their operations. Consequently, the use of process-oriented performance measurement systems gains importance. This study contributes to the development of a dashboard for the process of hip surgery using a case study design. We integrate strategic goals of hospital management and different stakeholders with the analysis of Business Process Management and Hospital Information Systems’ data. Process-oriented KPIs were integrated into the dashboard using a three-step approach. Dashboards enable healthcare organizations to put process-oriented performance measurement into practice

Applying machine learning to predict future adherence to physical activity programs.

BackgroundIdentifying individuals who are unlikely to adhere to a physical exercise regime has potential to improve physical activity interventions. The aim of this paper is to develop and test adherence prediction models using objectively measured physical activity data in the Mobile Phone-Based Physical Activity Education program (mPED) trial. To the best of our knowledge, this is the first to apply Machine Learning methods to predict exercise relapse using accelerometer-recorded physical activity data.MethodsWe use logistic regression and support vector machine methods to design two versions of a Discontinuation Prediction Score (DiPS), which uses objectively measured past data (e.g., steps and goal achievement) to provide a numerical quantity indicating the likelihood of exercise relapse in the upcoming week. The respective prediction accuracy of these two versions of DiPS are compared, and then numerical simulation is performed to explore the potential of using DiPS to selectively allocate financial incentives to participants to encourage them to increase physical activity.Resultswe had access to a physical activity trial data that were continuously collected every 60 sec every day for 9 months in 210 participants. By using the first 15 weeks of data as training and test on weeks 16-30, we show that both versions of DiPS have a test AUC of 0.9 with high sensitivity and specificity in predicting the probability of exercise adherence. Simulation results assuming different intervention regimes suggest the potential benefit of using DiPS as a score to allocate resources in physical activity intervention programs in reducing costs over other allocation schemes.ConclusionsDiPS is capable of making accurate and robust predictions for future weeks. The most predictive features are steps and physical activity intensity. Furthermore, the use of DiPS scores can be a promising approach to determine when or if to provide just-in-time messages and step goal adjustments to improve compliance. Further studies on the use of DiPS in the design of physical activity promotion programs are warranted.Trial registrationClinicalTrials.gov NCT01280812 Registered on January 21, 2011

Applications of lean thinking: a briefing document

This report has been put together by the Health and Care Infrastructure Research and Innovation Centre (HaCIRIC) at the University of Salford for the Department of Health. The need for the report grew out of two main simple questions, o Is Lean applicable in sectors other than manufacturing? o Can the service delivery sector learn from the success of lean in manufacturing and realise the benefits of its implementation?The aim of the report is to list together examples of lean thinking as it is evidenced in the public and private service sector. Following a review of various sources a catalogue of evidence is put together in an organised manner which demonstrates that Lean principles and techniques, when applied rigorously and throughout an entire organization/unit, they can have a positive impact on productivity, cost, quality, and timely delivery of services

Translating clinicians' beliefs into implementation interventions (TRACII) : a protocol for an intervention modeling experiment to change clinicians' intentions to implement evidence-based practice

Background: Biomedical research constantly produces new findings, but these are not routinely incorporated into health care practice. Currently, a range of interventions to promote the uptake of emerging evidence are available. While their effectiveness has been tested in pragmatic trials, these do not form a basis from which to generalise to routine care settings. Implementation research is the scientific study of methods to promote the uptake of research findings, and hence to reduce inappropriate care. As clinical practice is a form of human behaviour, theories of human behaviour that have proved to be useful in other settings offer a basis for developing a scientific rationale for the choice of interventions. Aims: The aims of this protocol are 1) to develop interventions to change beliefs that have already been identified as antecedents to antibiotic prescribing for sore throats, and 2) to experimentally evaluate these interventions to identify those that have the largest impact on behavioural intention and behavioural simulation. Design: The clinical focus for this work will be the management of uncomplicated sore throat in general practice. Symptoms of upper respiratory tract infections are common presenting features in primary care. They are frequently treated with antibiotics, and research evidence is clear that antibiotic treatment offers little or no benefit to otherwise healthy adult patients. Reducing antibiotic prescribing in the community by the "prudent" use of antibiotics is seen as one way to slow the rise in antibiotic resistance, and appears safe, at least in children. However, our understanding of how to do this is limited. Participants will be general medical practitioners. Two theory-based interventions will be designed to address the discriminant beliefs in the prescribing of antibiotics for sore throat, using empirically derived resources. The interventions will be evaluated in a 2 × 2 factorial randomised controlled trial delivered in a postal questionnaire survey. Two outcome measures will be assessed: behavioural intention and behavioural simulation.This study is funded by the European Commission Research Directorate as part of a multi-partner program: Research Based Education and Quality Improvement (ReBEQI): A Framework and tools to develop effective quality improvement programs in European healthcare. (Proposal No: QLRT-2001-00657)

The role of GP’s compensation schemes in diabetes care: evidence from panel data

The design of incentive schemes that improve quality of care is a central issue for the healthcare sector. Nowadays we observe many pay-for-performance programs, where payment is contingent on meeting indicators of provider effort, but also other alternative strategies have been introduced, for example programs rewarding physicians for participation in diseases management plans. Although it has been recognised that incentive-based remuneration schemes can have an impact on GP behaviour, there is still weak empirical evidence on the extent to which such programs influence health outcomes. We investigate the impact of financial incentives in Regional and Local Health Authority contracts for primary care in the Italian Region Emilia Romagna for the years 2003-05. We focus on avoidable hospitalisations (Ambulatory Care Sensitive Conditions) for patients affected by type 2 diabetes mellitus, for which the assumption of responsibility and the adoption of clinical guidelines are specifically rewarded. We estimate a panel count data model using a Negative Binomial distribution to test the hypothesis that, other things equal, patients under the responsibility of GPs receiving a higher share of their income through these programs are less likely to experience avoidable hospitalisations. Our findings support the hypothesis that financial transfers may contribute to improve quality of care, even when they are not based on the ex-post verification of performances.

Bayesian approaches to technology assessment and decision making

Until the mid-1980s, most economic analyses of healthcare technologies were based on decision theory and used decision-analytic models. The goal was to synthesize all relevant clinical and economic evidence for the purpose of assisting decision makers to efficiently allocate society's scarce resources. This was true of virtually all the early cost-effectiveness evaluations sponsored and/or published by the U.S. Congressional Office of Technology Assessment (OTA) (15), Centers of Disease Control and Prevention (CDC), the National Cancer Institute, other elements of the U.S. Public Health Service, and of healthcare technology assessors in Europe and elsewhere around the world. Methodologists routinely espoused, or at minimum assumed, that these economic analyses were based on decision theory (8;24;25). Since decision theory is rooted in—in fact, an informal application of—Bayesian statistical theory, these analysts were conducting studies to assist healthcare decision making by appealing to a Bayesian rather than a classical, or frequentist, inference approach. But their efforts were not so labeled. Oddly, the statistical training of these decision analysts was invariably classical, not Bayesian. Many were not—and still are not—conversant with Bayesian statistical approaches

Annotated Bibliography: Understanding Ambulatory Care Practices in the Context of Patient Safety and Quality Improvement.

The ambulatory care setting is an increasingly important component of the patient safety conversation. Inpatient safety is the primary focus of the vast majority of safety research and interventions, but the ambulatory setting is actually where most medical care is administered. Recent attention has shifted toward examining ambulatory care in order to implement better health care quality and safety practices. This annotated bibliography was created to analyze and augment the current literature on ambulatory care practices with regard to patient safety and quality improvement. By providing a thorough examination of current practices, potential improvement strategies in ambulatory care health care settings can be suggested. A better understanding of the myriad factors that influence delivery of patient care will catalyze future health care system development and implementation in the ambulatory setting

Applying psychological theory to evidence-based clinical practice : identifying factors predictive of managing upper respiratory tract infections without antibiotics

Background: Psychological models can be used to understand and predict behaviour in a wide range of settings. However, they have not been consistently applied to health professional behaviours, and the contribution of differing theories is not clear. The aim of this study was to explore the usefulness of a range of psychological theories to predict health professional behaviour relating to management of upper respiratory tract infections (URTIs) without antibiotics. Methods: Psychological measures were collected by postal questionnaire survey from a random sample of general practitioners (GPs) in Scotland. The outcome measures were clinical behaviour (using antibiotic prescription rates as a proxy indicator), behavioural simulation (scenario-based decisions to managing URTI with or without antibiotics) and behavioural intention (general intention to managing URTI without antibiotics). Explanatory variables were the constructs within the following theories: Theory of Planned Behaviour (TPB), Social Cognitive Theory (SCT), Common Sense Self-Regulation Model (CS-SRM), Operant Learning Theory (OLT), Implementation Intention (II), Stage Model (SM), and knowledge (a non-theoretical construct). For each outcome measure, multiple regression analysis was used to examine the predictive value of each theoretical model individually. Following this 'theory level' analysis, a 'cross theory' analysis was conducted to investigate the combined predictive value of all significant individual constructs across theories. Results: All theories were tested, but only significant results are presented. When predicting behaviour, at the theory level, OLT explained 6% of the variance and, in a cross theory analysis, OLT 'evidence of habitual behaviour' also explained 6%. When predicting behavioural simulation, at the theory level, the proportion of variance explained was: TPB, 31%; SCT, 26%; II, 6%; OLT, 24%. GPs who reported having already decided to change their management to try to avoid the use of antibiotics made significantly fewer scenario-based decisions to prescribe. In the cross theory analysis, perceived behavioural control (TPB), evidence of habitual behaviour (OLT), CS-SRM cause (chance/bad luck), and intention entered the equation, together explaining 36% of the variance. When predicting intention, at the theory level, the proportion of variance explained was: TPB, 30%; SCT, 29%; CS-SRM 27%; OLT, 43%. GPs who reported that they had already decided to change their management to try to avoid the use of antibiotics had a significantly higher intention to manage URTIs without prescribing antibiotics. In the cross theory analysis, OLT evidence of habitual behaviour, TPB attitudes, risk perception, CS-SRM control by doctor, TPB perceived behavioural control and CS-SRM control by treatment entered the equation, together explaining 49% of the variance in intention. Cnclusion: The study provides evidence that psychological models can be useful in understanding and predicting clinical behaviour. Taking a theory-based approach enables the reation of a replicable methodology for identifying factors that predict clinical behaviour. Hwever, a number of conceptual and methodological challenges remain