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Background: In primary care risk stratification, automated algorithms do not consider the same factors as providers. The process of adjudication, in which providers review and adjust algorithm-derived risk scores, may improve the prediction of adverse outcomes. Objective: We assessed the patient factors that influenced provider adjudication behavior and evaluated the performance of an adjudicated risk model against a commercial algorithm. Design: (1) Structured interviews with primary care providers (PCP) and multivariable regression analysis and (2) receiver operating characteristic curves (ROC) with sensitivity analyses. Participants: Primary care patients aged 18 years and older with an adjudicated risk score. Approach and Main Measures: (1) Themes from structured interviews and discrete variables associated with provider adjudication behavior; (2) comparison of concordance statistics and sensitivities between risk models. Key Results: 47,940 patients were adjudicated by PCPs in 2018. Interviews revealed that, in adjudication, providers consider disease severity, presence of self-management skills, behavioral health, and whether a risk score is actionable. Provider up-scoring from the algorithmic risk score was significantly associated with patient male sex (OR 1.24, CI 1.15–1.34), age > 65 (OR 2.55, CI 2.24–2.91), Black race (1.26, CI 1.02–1.55), polypharmacy >10 medications (OR 4.87, CI 4.27–5.56), a positive depression screen (OR 1.57, CI 1.43–1.72), and hemoglobin A1c >9 (OR 1.89, CI 1.52–2.33). Overall, the adjudicated risk model performed better than the commercial algorithm for all outcomes: ED visits (c-statistic 0.689 vs. 0.684, p < 0.01), hospital admissions (c-statistic 0.663 vs. 0.649, p < 0.01), and death (c-statistic 0.753 vs. 0.721, p < 0.01). When limited to males or seniors, the adjudicated models displayed either improved or non-inferior performance compared to the commercial model. Conclusions: Provider adjudication of risk stratification improves model performance because providers have a personal understanding of their patients and are able to apply their training to clinical decision-making. [ABSTRACT FROM AUTHOR]

We conducted a meta-synthesis of five different studies that developed, tested, and implemented new technologies for the purpose of collecting observations of daily living (ODL). From this synthesis, we developed a model to explain user motivation as it relates to ODL collection. We describe this model that includes six factors that motivate patients' collection of ODL data: usability, illness experience, relevance of ODL, information technology infrastructure, degree of burden, and emotional activation. We show how these factors can act as barriers or facilitators to the collection of ODL data and how interacting with care professionals and sharing ODL data may also influence ODL collection, health-related awareness, and behavior change. The model we developed and used to explain ODL collection can be helpful to researchers and designers who study and develop new, personal health technologies to empower people to improve their health. [ABSTRACT FROM AUTHOR]

Background: The quality of care for diabetes continues to fall short of recommended guidelines and results. Models for improving the care of chronic illnesses advocate a multidisciplinary team approach. Yet little is known about the effectiveness of such models in an academic setting with a diverse patient population and resident physicians participating in clinical care.Objective: To implement a chronic illness management (CIM) practice within an academic setting with part-time providers, and evaluate its impact on the completion of diabetes-specific care processes and on the achievement of recommended outcomes for patients with diabetes mellitus.Design: Retrospective cohort studySubjects: Patients with the diagnosis of diabetes mellitus who receive their primary care in an academic general internal medicine resident-faculty practice.Measurements: Process and outcomes measures in patients exposed to the CIM practice were compared with non-exposed patients receiving usual care.Main Results: Five hundred and sixty-five patients met inclusion criteria. Patients in the CIM practice experienced a significant increase in completion of care processes compared to control patients for measurement of annual low-density lipoprotein (LDL) cholesterol (OR 3.1, 95% CI 1.7-5.7), urine microalbumin (OR 3.3, 95% CI 2.1-5.5), blood pressure (OR 1.8, 95% CI 1.1-2.8), retinal examination (OR 1.9, 95% CI 1.3-2.7), foot monofilament examination (OR 4.2, 95% CI 3.0-6.1) and administration of pneumococcal vaccination (OR 5.2, 95% CI 3.0-9.3). CIM-exposed patients were also more likely to achieve improvements in clinical outcomes of glycemic and blood pressure control reflected by hemoglobin A1c less than 7.0% (OR 1.7, 95% CI 1.02-3) and blood pressure less than 130/80 (OR 2.8, 95% CI 2.1-4.5) compared to controls.Conclusions: A systematic chronic care model can be successfully integrated into an academic general internal medicine practice and may result in improved processes of care and some clinical outcomes for diabetic patients. This study provides a model for further hypothesis generation and more rigorous testing of the quality benefits of structured chronic illness care in diverse outpatient practices. [ABSTRACT FROM AUTHOR]

Background: In populations with chronic illness, outcomes improve with the use of care models that integrate clinical information, evidence-based treatments, and proactive management of care. Health information technology is believed to be critical for efficient implementation of these chronic care models. Health care organizations have implemented information technologies, such as electronic medical records, to varying degrees. However, considerable uncertainty remains regarding the relative impact of specific informatics technologies on chronic illness care.Objective: To summarize knowledge and increase expert consensus regarding informatics components that support improvement in chronic illness care.Design: A systematic review of the literature was performed. "Use case" models were then developed, based on the literature review, and guidance from clinicians and national quality improvement projects. A national expert panel process was conducted to increase consensus regarding information system components that can be used to improve chronic illness care.Results: The expert panel agreed that informatics should be patient-centered, focused on improving outcomes, and provide support for illness self-management. They concurred that outcomes should be routinely assessed, provided to clinicians during the clinical encounter, and used for population-based care management. It was recommended that interactive, sequential, disorder-specific treatment pathways be implemented to quickly provide clinicians with patient clinical status, treatment history, and decision support.Conclusions: Specific informatics strategies have the potential to improve care for chronic illness. Software to implement these strategies should be developed, and rigorously evaluated within the context of organizational efforts to improve care. [ABSTRACT FROM AUTHOR]

Background: The care of patients with complex illnesses requires careful management, but systems of care management (CM) vary in their structure and effectiveness.Objective: To create a framework identifying components of broad-based CM interventions and validate the framework, including using this framework to evaluate the contribution of varying components on outcomes of patients with chronic illness.Design: We create the framework using retrospective information about CM activities and services over 12 months and categorize it using cluster and factor analysis. We then validate this framework through content and criterion techniques. Content validity is assessed through a Delphi study and criterion validity through relationship of the dosage measures and patterns of care to process and outcomes measures.Participants: Patients with diabetes and/or cardiovascular disease receiving CM services in a model known as Care Management Plus implemented in primary care.Results: Six factors of CM activity were identified, including a single dosage summary measure and 5 separate patterns of care. Of these, the overall dosage summary measure, face-to-face time, duration of follow-up, and breadth of services were all related to improved processes for hemoglobin A1c and LDL testing and control. Brief intense patterns of care and high face-to-face care manager time were also related to improved outcomes.Conclusions: Using this framework, we isolate components of a CM intervention directly related to improved process of care or patient outcomes. Current efforts to structure CM to include face-to-face time and multiple diseases are discussed. [ABSTRACT FROM AUTHOR]

The article comments on the article "Which complex patients should be referred for intensive care management? A mixed methods analysis" by R.W. Grant and colleagues published within the issue. It mentions that elicited factors that determined appropriateness were then used to try to predict patients who may be better candidates for the care managers in these programs. It suggests proactive implementation of prediction for both needs of patients and their appropriateness for certain services.