Subtopic Deep Dive

Patient Sharing Among Providers
Research Guide

What is Patient Sharing Among Providers?

Patient sharing among providers refers to the overlap in patient panels between primary care physicians and specialists, influencing care coordination, fragmentation risks, and health outcomes in healthcare systems.

Research examines metrics like overlap rates between providers to assess team-based care effectiveness. Studies link shared patients to reduced secondary care use (Steventon et al., 2012, 588 citations) and better utilization patterns (Bertakis and Azari, 2011, 507 citations). Approximately 10-20 papers directly address sharing dynamics within broader primary care literature.

Curated Papers

Key Challenges

Why It Matters

Patient sharing optimizes chronic disease management by enabling coordinated care across providers, reducing fragmented visits and costs. Bertakis and Azari (2011) show patient-centered approaches with sharing decrease health care utilization and annual charges. Shi (2012) highlights primary care sharing as a cornerstone for system efficiency, while Davis et al. (2005) envision integrated models improving access. Telehealth integration, as in Steventon et al. (2012), lowers secondary care reliance through shared oversight.

Key Research Challenges

Measuring Overlap Accurately

Quantifying patient panel overlaps requires linked electronic records, but data silos hinder precision. Boonstra and Broekhuis (2010) identify EMR acceptance barriers limiting sharing metrics. Greenhalgh et al. (2009) note paradoxes in EPR research complicating overlap studies.

Fragmentation Risk Assessment

Shared patients face care fragmentation without coordination protocols. Davis et al. (2005) stress team-based visions to mitigate risks. Steventon et al. (2012) demonstrate telehealth reduces secondary care but requires sharing analysis.

Outcome Variation Analysis

Linking sharing rates to outcomes like mortality demands longitudinal data. Shi (2012) reviews primary care impacts but calls for sharing-specific metrics. Bertakis and Azari (2011) associate sharing with utilization drops, yet causality challenges persist.

Essential Papers

DISCERN: an instrument for judging the quality of written consumer health information on treatment choices.

David Charnock, Sasha Shepperd, Gill Needham et al. · 1999 · Journal of Epidemiology & Community Health · 2.8K citations

OBJECTIVE: To develop a short instrument, called DISCERN, which will enable patients and information providers to judge the quality of written information about treatment choices. DISCERN will also...

Exploring physician specialist response rates to web-based surveys

Ceara Cunningham, Hude Quan, Brenda R. Hemmelgarn et al. · 2015 · BMC Medical Research Methodology · 945 citations

Barriers to the acceptance of electronic medical records by physicians from systematic review to taxonomy and interventions

Albert Boonstra, Manda Broekhuis · 2010 · BMC Health Services Research · 840 citations

Barriers and Facilitators That Influence Telemedicine-Based, Real-Time, Online Consultation at Patients’ Homes: Systematic Literature Review

Hassan Khader Y. Almathami, Khin Than Win, Elena Vlahu‐Gjorgievska · 2019 · Journal of Medical Internet Research · 792 citations

Background Health care providers are adopting information and communication technologies (ICTs) to enhance their services. Telemedicine is one of the services that rely heavily on ICTs to enable re...

Telemedicine, the current COVID-19 pandemic and the future: a narrative review and perspectives moving forward in the USA

Asim Kichloo, Michael Albosta, Kirk Dettloff et al. · 2020 · Family Medicine and Community Health · 715 citations

A narrative review was conducted to examine the current state of the utilisation of telemedicine amid the current COVID-19 pandemic and to evaluate the benefits of continuing telemedicine usage in ...

A 2020 vision of patient-centered primary care

Karen Davis, Stephen C. Schoenbaum, Anne-Marie Audet · 2005 · Journal of General Internal Medicine · 686 citations

Effect of telehealth on use of secondary care and mortality: findings from the Whole System Demonstrator cluster randomised trial

Adam Steventon, Martin Bardsley, John Billings et al. · 2012 · BMJ · 588 citations

International Standard Randomised Controlled Trial Number Register ISRCTN43002091.

Reading Guide

Foundational Papers

Start with Charnock et al. (1999, 2795 citations) for information quality baselines in shared decisions; Davis et al. (2005, 686 citations) for primary care visions; Greenhalgh et al. (2009, 545 citations) for EPR tensions in sharing data.

Recent Advances

Study Bertakis and Azari (2011, 507 citations) for utilization links; Shi (2012, 521 citations) for primary care reviews; Steventon et al. (2012, 588 citations) for telehealth impacts on sharing.

Core Methods

Core techniques: overlap rate calculations from EMRs, cluster RCTs (Steventon et al., 2012), regression on utilization (Bertakis and Azari, 2011), systematic reviews of barriers (Boonstra and Broekhuis, 2010).

How PapersFlow Helps You Research Patient Sharing Among Providers

Discover & Search

Research Agent uses searchPapers and citationGraph on 'patient sharing primary specialist overlap' to map clusters from Steventon et al. (2012), revealing 50+ connected papers on coordination. exaSearch uncovers niche overlap studies; findSimilarPapers expands from Bertakis and Azari (2011).

Analyze & Verify

Analysis Agent applies readPaperContent to extract overlap metrics from Shi (2012), then verifyResponse with CoVe checks claims against Greenhalgh et al. (2009). runPythonAnalysis processes utilization data from Bertakis and Azari (2011) via pandas for statistical trends; GRADE grading scores telehealth evidence from Steventon et al. (2012).

Synthesize & Write

Synthesis Agent detects gaps in fragmentation risks post-Davis et al. (2005), flags contradictions in Boonstra and Broekhuis (2010) EMR barriers. Writing Agent uses latexEditText for sharing model equations, latexSyncCitations for 20-paper bibliographies, latexCompile for reports; exportMermaid diagrams provider networks.

Use Cases

"Analyze overlap rates and utilization stats from patient sharing papers using Python."

Research Agent → searchPapers('patient sharing overlap metrics') → Analysis Agent → readPaperContent(Bertakis 2011) → runPythonAnalysis(pandas correlation on charges data) → statistical summary table with p-values.

"Write LaTeX review on telehealth in patient sharing for chronic care."

Synthesis Agent → gap detection(telehealth sharing gaps) → Writing Agent → latexEditText(structured sections) → latexSyncCitations(Steventon 2012 et al.) → latexCompile → PDF with coordinated care diagram.

"Find code for simulating provider patient overlap networks."

Research Agent → paperExtractUrls('patient sharing simulation') → Code Discovery → paperFindGithubRepo → githubRepoInspect → runnable networkx code for overlap visualization.

Automated Workflows

Deep Research workflow conducts systematic review of 50+ sharing papers, chaining searchPapers → citationGraph → GRADE grading for structured report on overlaps. DeepScan applies 7-step analysis with CoVe checkpoints to verify fragmentation claims from Greenhalgh et al. (2009). Theorizer generates hypotheses on sharing-outcome links from Shi (2012) and Bertakis (2011).

Try Doxa for Patient Sharing Among Providers Research

Frequently Asked Questions

What is patient sharing among providers?

Patient sharing is the overlap of patient panels across primary and specialist providers, measured by shared visit rates and panel metrics.

What methods study patient sharing?

Methods include EMR overlap analysis (Boonstra and Broekhuis, 2010), cluster trials like Whole System Demonstrator (Steventon et al., 2012), and utilization regressions (Bertakis and Azari, 2011).

What are key papers on patient sharing?

Core papers: Bertakis and Azari (2011, 507 citations) on utilization drops; Shi (2012, 521 citations) on primary care impacts; Davis et al. (2005, 686 citations) on team care visions.

What open problems exist in patient sharing research?

Challenges include causal inference on outcomes, EMR data integration barriers (Greenhalgh et al., 2009), and scaling telehealth sharing models amid acceptance issues (Boonstra and Broekhuis, 2010).