Advancing Family Medicine in a Model Unit: A Living Lab for Health Care Design and Innovation
Paul, M. M.; Matthews, M. R.; Greaney, G. B.; Wallenstein, D. W.; Greenwood, J. D.; Spaulding, T.; Eckdahl, J. S.; Rushlow, D. R.
Show abstract
IntroductionThe population in need of primary care is rapidly growing and increasingly complex with respect to chronic disease burden. We must develop alternative and more efficient approaches to managing patients if we are to increase access to care without sacrificing continuity; however, there is little guidance for innovation strategies at the practice level. MethodsThe Mayo Clinic Department of Family Medicine engaged in a 2-year multistage planning process to develop plans for a Model Unit (MU) to identify opportunities for innovation to improve daily practice. The purpose of the MU is to operate as a "living lab" capable of driving continuous advancements within the context of a real-world health system with the goal of delivering high-quality care to a greater number of patients. ResultsKey lessons from the planning stage led to the development of a contextualized, incremental, and continuous approach to design and innovation. In its first phase, the MU includes 3 interventions that are novel to the unit itself, including nurse-led hypertension management, incorporating telehealth visits into routine clinician schedules, and ambient documentation to replace clinician-generated visit notes. We present a description of the overall MU approach including early-stage implementation findings and our evaluation strategy. ConclusionsThe MU structure is a generalizable model for identifying opportunities and operationalizing practice improvement activities in a strategic and pragmatic way that incorporates real-time feedback from clinicians and staff with the expectation of continuous and phased evolution.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Essential Indicators of Quality in Primary Care Settings: An Evidence-Based, Structured, Expert Approach 95%
- Development of the Tool for Advancing Practice Performance, a practice-level survey to assess primary care structures and processes 94%
- Clinical code sets and the problem of redundancy in code set repositories 92%
Similar papers in this journal
- User Testing of a Diagnostic Decision Support System with Machine-assisted Chart Review to Facilitate Clinical Genomic Diagnosis 94%
- Connecting Artificial Intelligence and Primary Care Challenges: Findings from a Multi-Stakeholder Collaborative Consultation 94%
- Development of a customised data management system for a COVID-19-adapted colorectal cancer pathway 91%
Similar papers in this journal
- The PUPPY Study - Protocol for a Longitudinal Mixed Methods Study Exploring Problems Coordinating and Accessing Primary Care for Attached and Unattached Patients Exacerbated During the COVID-19 Pandemic Year 93%
- Scientific hypothesis generation process in clinical research: a secondary data analytic tool versus experience study protocol 92%
- Strategies and Tools for electronic health records and physician workflow alignment: A scoping review protocol 92%
Similar papers in this journal
- The MultimorbiditY COllaborative Medication Review And DEcision Making (MyComrade) study: A protocol for a cross-border pilot cluster randomised controlled trial 93%
- Evaluating Diuretics in Normal Care (EVIDENCE): A feasibility report of a pilot cluster randomised trial of prescribing policy in primary care to compare the effectiveness of thiazide-type diuretics in hypertension 91%
- A complex ePrescribing-based Anti-Microbial Stewardship (ePAMS+) intervention for hospitals combining technological and behavioural components: protocol for a feasibility trial 91%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.