# Shareable, Interoperable Clinical decision Support for Older Adults: Advancing Fall assessment and Prevention Patient-Centered Outcomes Research Findings into Diverse Primary Care Practices (ASPIRE)

> **NIH AHRQ U18** · BRIGHAM AND WOMEN'S HOSPITAL · 2020 · $499,985

## Abstract

ABSTRACT
Community-based falls do not have to be a part of healthy aging for older adults. However, fall and injury
incidence in the community is a persistent problem amongst community dwellers over the age of 65 years, a
rapidly growing population. Sequelae from falls include hip and other fractures, head injuries, fear of falling, and
nursing home hospitalization; traumatic and often costly outcomes. Older adults who are at risk for falling are
encouraged to take actions to reduce their chances of falling.
Our research team is participating in an ongoing community-based fall prevention pragmatic trial, Strategies to
Reduce Injuries and Develop Confidence in Elders (STRIDE), which aims to integrate patient centered fall
prevention evidence into primary care settings through use of Falls Care Managers (FCM) and clinical decision
support (CDS) that promotes shared fall prevention decision-making. STRIDE is funded by the National Institute
of Aging and Patient Centered Outcome Research Institute cooperative agreement number 5U01AG048270. The
intended goal of CDS is to use clinical knowledge in the context of patient-specific evidence to support
healthcare providers in the process of decision-making. At the center of the STRIDE intervention is software
used by an FCM and members of the clinical care team to link patient-specific risk factors to tailored evidence-
based interventions to prevent fall-related injuries. The fall prevention plan is then refined using motivational
interviewing by the FCM in collaboration with the patient and the primary care team.
The current version of the STRIDE software includes the algorithms needed to support evidence-based fall
prevention care including shared decision-making with patients, but it also has a number of limitations: 1) it is
built in REDCap as a standalone database, separate from the electronic health record (EHR) system used to
document patient assessments and other patient care activities, 2) it has been built as an electronic form that
is not dynamic and therefore creates unnecessary documentation burden, 3) it does not support busy clinic
workflows where assessments must be quickly documented and CDS provided in the context of care provision.
These limitations preclude use of the software outside of controlled research settings where there are
additional staff to overcome (or “workaround”) the software limitations. Through this project entitled, Advancing
Fall ASsessment and PreventIon PatIent-Centered Outcomes REsearch Findings into Diverse Primary Care
Practices (ASPIRE), we will overcome these barriers. The goal of the ASPIRE project is to apply the
Agency for Healthcare Research & Quality (AHRQ) CDS Connect authoring tool to develop shareable,
standards-based fall prevention software that can be posted to the CDS Connect repository. Achieving
this goal will address limitations and support wider dissemination of the STRIDE CDS in primary care to
support patient and provider fall prevention deci...

## Key facts

- **NIH application ID:** 10023772
- **Project number:** 1U18HS027557-01
- **Recipient organization:** BRIGHAM AND WOMEN'S HOSPITAL
- **Principal Investigator:** Patricia C Dykes
- **Activity code:** U18 (R01, R21, SBIR, etc.)
- **Funding institute:** AHRQ
- **Fiscal year:** 2020
- **Award amount:** $499,985
- **Award type:** 1
- **Project period:** 2020-08-01 → 2022-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10023772

## Citation

> US National Institutes of Health, RePORTER application 10023772, Shareable, Interoperable Clinical decision Support for Older Adults: Advancing Fall assessment and Prevention Patient-Centered Outcomes Research Findings into Diverse Primary Care Practices (ASPIRE) (1U18HS027557-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10023772. Licensed CC0.

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