The impact of redesigning care processes on quality of care: a systematic review

Background: This literature review evaluates the current state of knowledge about the impact of process redesign on the quality of healthcare. Methods: Pubmed, CINAHL, Web of Science and Business Premier Source were searched for relevant studies published in the last ten years [20042014]. To be included, studies had to be original research, published in English with a before-and-after study design, and be focused on changes in healthcare processes and quality of care. Studies that met the inclusion criteria were independently assessed for excellence in reporting by three reviewers using the SQUIRE checklist. Data was extracted using a framework developed for this review. Results: Reporting adequacy varied across the studies. Process redesign interventions were diverse, and none of the studies described their effects on all dimensions of quality defined by the Institute of Medicine. Conclusions: The results of this systematic literature review suggests that process redesign interventions have positive effects on certain aspects of quality. However, the full impact cannot be determined on the basis of the literature. A wide range of outcome measures were used, and research methods were limited. This review demonstrates the need for further investigation of the impact of redesign interventions on the quality of healthcare. Keywords: Process redesign, Quality of care, Healthcare processes, Systematic revie

Disentangling Population Health Management Initiatives in Diabetes Care: A Scoping Review

Introduction: Population Health Management (PHM) focusses on keeping the whole population as healthy as possible. As such, it could be a promising approach for long-term health improvement in type 2 diabetes. This scoping review aimed to examine the extent to which and how PHM is used in the care for people with type 2 diabetes. Methods: PubMed, Web of Science, and Embase were searched between January 2000 and September 2021 for papers on self-reported PHM initiatives for type 2 diabetes. Eligible initiatives were described using the analytical framework for PHM. Results: In total, 25 studies regarding 18 PHM initiatives for type 2 diabetes populations were included. There is considerable variation in whether and how the PHM steps are operationalized in existing PHM initiatives. Population identification, impact evaluation, and quality improvement processes were generally part of the PHM initiatives. Triple Aim assessment and risk stratification actions were scarce or explained in little detail. Moreover, cross-sector integration is key in PHM but scarce in practice. Conclusion: Operationalization of PHM in practice is limited compared to the PHM steps described in the analytical framework. Extended risk stratification and integration efforts would contribute to whole-person care and further health improvements within the population

A risk score including body mass index, glycated haemoglobin and triglycerides predicts future glycaemic control in people with type 2 diabetes

AimTo identify, predict and validate distinct glycaemic trajectories among patients with newly diagnosed type 2 diabetes treated in primary care, as a first step towards more effective patient-centred care. MethodsWe conducted a retrospective study in two cohorts, using routinely collected individual patient data from primary care practices obtained from two large Dutch diabetes patient registries. Participants included adult patients newly diagnosed with type 2 diabetes between January 2006 and December 2014 (development cohort, n=10528; validation cohort, n=3777). Latent growth mixture modelling identified distinct glycaemic 5-year trajectories. Machine learning models were built to predict the trajectories using easily obtainable patient characteristics in daily clinical practice. ResultsThree different glycaemic trajectories were identified: (1) stable, adequate glycaemic control (76.5% of patients); (2) improved glycaemic control (21.3% of patients); and (3) deteriorated glycaemic control (2.2% of patients). Similar trajectories could be discerned in the validation cohort. Body mass index and glycated haemoglobin and triglyceride levels were the most important predictors of trajectory membership. The predictive model, trained on the development cohort, had a receiver-operating characteristic area under the curve of 0.96 in the validation cohort, indicating excellent accuracy. ConclusionsThe developed model can effectively explain heterogeneity in future glycaemic response of patients with type 2 diabetes. It can therefore be used in clinical practice as a quick and easy tool to provide tailored diabetes care

Profiling Patients' Healthcare Needs to Support Integrated, Person-Centered Models for Long-Term Disease Management (Profile): Research Design

<p class="p1"><strong>Background: </strong>This article presents the design of PROFILe, a study investigating which (bio)medical and non-(bio)medical patient characteristics should guide more tailored chronic care. Based on this insight, the project aims to develop and validate ‘patient profiles’ that can be used in practice to determine optimal treatment strategies for subgroups of chronically ill with similar healthcare needs and preferences. <p class="p2"><strong>Methods/Design: </strong>PROFILe is a practice-based research comprising four phases. The project focuses on patients with type 2 diabetes. During the first study phase, patient profiles are drafted based on a systematic literature research, latent class growth modeling, and expert collaboration. In phase 2, the profiles are validated from a clinical, patient-related and statistical perspective. Phase 3 involves a discrete choice experiment to gain insight into the patient preferences that exist per profile. In phase 4, the results from all analyses are integrated and recommendations formulated on which patient characteristics should guide tailored chronic care. <p class="p2"><strong>Discussion: </strong>PROFILe is an innovative study which uses a uniquely holistic approach to assess the healthcare needs and preferences of chronically ill. The patient profiles resulting from this project must be tested in practice to investigate the effects of tailored management on patient experience, population health and costs