# R18 Closed Loop Diagnostics : AHRQ R18 Patient Safety Learning Laboratories

> **NIH AHRQ R18** · HARVARD MEDICAL SCHOOL · 2020 · $611,451

## Abstract

Abstract
Diagnostic errors in primary care often are due to failures to follow up (“close the loop”) on diagnostic tests,
referrals, and symptoms. More specifically, (1) diagnostic tests and referrals often are not completed, (2)
results of diagnostic tests and referrals often are not conveyed to patients and their primary care physicians,
and (3) primary care physicians frequently are not informed when symptoms evolve that could alter a
diagnosis. To address these gaps, our multidisciplinary team of clinicians, systems engineers, and patients will
use an engineering life cycle to design systems to decrease the number of associated diagnostic errors by
preventing each of these types of failures in a large primary care practice. Our proposed research employs
innovative evidenced-based system engineering (SE) methods to develop highly reliable and robust processes
in other industries, but not yet widely adopted in healthcare. Our specific aims are as follows:
(Aim 1) Design, develop, and refine highly reliable “closed loop” systems for diagnostic tests and referrals that
ensure these occur within clinically- and patient-important timeframes;
(Aim 2) Design, develop, and refine a highly reliable “closed loop” symptom monitoring system to ensure
clinicians receive information about evolving symptoms of concern; and
(Aim 3) Ensure broader generalizability of results of Aims 1 and 2 by ensuring these new processes are
effective in a community health center in an underserved community, a large telemedicine system, and a
representative range of simulated other health system settings and populations.
Our research hypothesis is that a methodical systems approach to closing loops on diagnostic processes will
measurably improve timely completion of ordered tests, referrals, and symptom reports, leading to reductions
in diagnostic errors. Key innovations of our project are the use of high reliability and human factors methods,
inclusion of patients and clinicians from other practices throughout the engineering process, and combined use
of statistical, qualitative, and computer modeling methods to estimate improvements both in our primary site
and more broadly. Projected results include increased completion of high-risk diagnostic tests, referrals, and
concerning symptoms, in turn resulting in reduced diagnostic errors, negative health outcomes, and associated
costs. Learning outcomes include improved understanding of closed loop diagnostic and monitoring problems
in primary care, patient engagement in solutions to such problems, and the utility of systems engineering to
important healthcare problems. Our project responds to 4 of the 8 Institute of Medicine recommendations from
their Improving Diagnosis in Healthcare report, the President's Council of Advisors on Science and Technology
recommendation that systems engineering be applied to primary care problems, and the PSLL solicitation
emphases on value-based care, safety, patient engagement, and provider bur...

## Key facts

- **NIH application ID:** 10015291
- **Project number:** 5R18HS027282-02
- **Recipient organization:** HARVARD MEDICAL SCHOOL
- **Principal Investigator:** JAMES C BENNEYAN
- **Activity code:** R18 (R01, R21, SBIR, etc.)
- **Funding institute:** AHRQ
- **Fiscal year:** 2020
- **Award amount:** $611,451
- **Award type:** 5
- **Project period:** 2019-09-30 → 2023-09-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10015291

## Citation

> US National Institutes of Health, RePORTER application 10015291, R18 Closed Loop Diagnostics : AHRQ R18 Patient Safety Learning Laboratories (5R18HS027282-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10015291. Licensed CC0.

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