2015 Meeting Theme: Considering Data Quality in Health Services

2015 Meeting Theme:
Considering Data Quality in Health Services Research
Save the Date – Monday, March 16, 2015 – 9:00 am – 3:00 pm
University of Utah Health Sciences Education Building
Health services research is a multidisciplinary field of inquiry that examines the use, costs,
accessibility, delivery, organization, financing, and outcomes of health care services to increase
knowledge and understanding of the structure, processes, and effects of health services for
individuals and populations. Abstracts reporting results of studies that address data quality
using EHRs, ancillary systems (e.g., lab results), patient reported measures are encouraged.
Abstract Title:
Using A Systematic Quality-Based Approach to Integrate a Robust
Depression Risk Module into an Existing Patient Activation Tool
Preferred method of presentation: (check all that apply)
Oral Presentation
Poster Presentation
Presenter: Jason Scott
Email: [email protected]
Org Affiliation:
Research Design:
Degree(s): MPH, MPP
Phone: 801-442-3834
Intermountain Healthcare, Institute for Health Care Delivery
An effective patient engagement tool must provide accurate
information to patients in a way that is easy to understand,
motivational, and actionable. Intermountain Healthcare
undertook the development of a depression-based risk
factor module to be integrated into the IndiGO patient
activation tool. This tool is designed to be used by providers
and care teams based mainly in a primary care setting. This
initiative followed an established approach engaging
necessary stakeholders to ensure the resulting tool is robust
and easy to use.
A systematic approach was used to ensure high data quality
and usefulness of the final module. First, patient and
clinician interviews were conducted to determine which
depression-related factors concern patients most and what
potential interventions patients would be willing to adopt.
Second, a provider-led team contributed clinical expertise to
informed areas and interventions of interest. This process
was undertaken to ensure the appropriate clinical factors
were include in the analysis to ensure the resulting
outcomes would be the most likely to engage patients.
Third, necessary data elements were identified by the
clinical and analytic teams and assessed for data accuracy
and completeness. Finally, the provider-led team undertook
a formal validation of the tool comparing modeled results to
a sample of patient charts. Modeling was conducted by
Archimedes model using logistic models with patient-level
clinical data. Data consisted of patient demographics,
Data Sources:
Implications for
Policy and/or Practice:
hospital and clinic visit information, depression diagnosis
history and episode data, PHQ-9 survey scores, and
depression related medication orders.
The study consisted of patients identified within
Intermountain Healthcare’s depression registry between
2000 and 2012. To ensure data accuracy, specific
combinations of patient encounters were required to be
included in the cohort. This consists of patients with
depression indicated in the EMR problem list, an inpatient
or ED visit with a diagnosis of depression, at least two office
visits with a diagnosis of depression within 12 months, or
patients with a clinic/ED visit paired with a prescription for
antidepressants. The resulting population consisted of
353,674 patients.
Interviews, focus groups, expert opinion, EDW, patient
Mixed methods
The patient activation tool interactively shows patients how
depression treatment, exercise, and treatment persistence
are likely to impact depression response, remission, and
relapse. Suicidality was modeled, but eventually omitted
because the team felt the included interventions did not
show enough benefit to effectively engage patients.
Interventions for Depression medication and psychotherapy
were combined into a single intervention because there was
insufficient data to accurately model the two components
independently. Adjustments were made based on data
availability and the effectiveness of specific factors to
engage patients. In addition, a rigorous validation process
encouraged confidence in the tool and accelerated adoption
by providers. This information is currently available in 65
Intermountain clinics and is being run on 320,822 patients.
Adopting a formal approach to operational initiatives helps
ensure development of tools that are accurate, meaningful
and effective. Data-based patient engagement tools require
much more than data perspective and expertise to
implement. Other key stakeholders including providers and
patients are essential to understand what factors are most
likely to inform effectiveness, usability, and impact.
Systematically approaching the design, relevance,
validation, and implementation of the tool is an important
factor determining adoption to impact patient health and
Analysis completed
Status of Analyses:
Please submit your structured abstract (limit 2 pages) no later than January 16, 2015
Submit via e-mail to: [email protected]
Patient Centered Research Methods Core