# Answering the call to engage patients and families in the diagnostic process: A new patient-centered approach using health information transparency to identify diagnostic breakdowns in ambulatory care

> **NIH AHRQ R01** · BETH ISRAEL DEACONESS MEDICAL CENTER · 2020 · $494,222

## Abstract

Diagnostic errors in ambulatory care present a vexing challenge, at least in part because of limited
measurements to identify events that unfold over time and space (various healthcare interactions). Although
the NAM report emphasizes the patient perspective, traditional diagnostic error measures do not. New data
suggest that patient experiences of diagnostic error reflect themes virtually absent from current metrics:
ignoring patient knowledge, disrespect, and other unprofessional behavior and communication barriers.
Lack of patient-centered metrics truncates organizational learning and the ability to more meaningfully
prevent future diagnostic errors, which levy an enormous burden on patients -- particularly the sickest
children and adults. Fortunately, as health information transparency spreads, there are new ways to engage
patients in the diagnostic process. Sharing visit notes with patients through the patient portal provides them
with a written record of their ambulatory experiences, and represents a rich but relatively untapped existing
source for patients to help identify breakdowns in diagnosis and care. Growing evidence demonstrates the
unique knowledge of patients and their ability to identify such breakdowns. Over 40 million patients across
the US can now access open notes. Studies demonstrate patient safety and engagement benefits from
open notes, especially among more vulnerable patients. Working firsthand on implementation and
measurement of the effects of shared visit notes over the last decade, our team has gained a nuanced
understanding of opportunities and challenges related to transparent notes in virtually every medical field.
We have also innovated two new approaches, enabling patients to a) contribute to notes and b) to report on
potential note errors. Our research shows frequent patient-reported breakdowns related to the diagnostic
process. Partnering with patients/families, health services researchers, and diagnostic error experts, we are
now uniquely poised to: 1a) Establish a new patient-centered framework co-designed with patients/families
and care partners to measure and categorize patient-reported diagnostic breakdowns (PRDBs), and b)
apply this new analytic tool to establish the incidence, types, and contributing factors to PRDBs in
ambulatory care using 2 large and unique existing databases; 2) Develop and implement a new EHR portal-
based method enabling chronically ill patients and their families to a) contribute to the visit note and
diagnostic process and b) identify and report diagnostic breakdowns using existing EHR data; and 3)
Assess the use and impact of this method on the diagnostic process measuring safety (incidence/types of
PRDBs among chronically ill patients), implementation, and stakeholder experience outcomes. Our findings
will improve our understanding of ambulatory diagnostic breakdowns and establish a new method for
organizations to systematically partner with patients in the diagnostic process....

## Key facts

- **NIH application ID:** 10016291
- **Project number:** 5R01HS027367-02
- **Recipient organization:** BETH ISRAEL DEACONESS MEDICAL CENTER
- **Principal Investigator:** SIGALL BELL
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** AHRQ
- **Fiscal year:** 2020
- **Award amount:** $494,222
- **Award type:** 5
- **Project period:** 2019-09-30 → 2022-09-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10016291

## Citation

> US National Institutes of Health, RePORTER application 10016291, Answering the call to engage patients and families in the diagnostic process: A new patient-centered approach using health information transparency to identify diagnostic breakdowns in ambulatory care (5R01HS027367-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10016291. Licensed CC0.

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