AHRQ Multiple Chronic Conditions Research Network

This study is provided by the AHRQ Multiple Chronic Conditions Research Network.

Enhancing Analytic Abilities to Identify Complex Patients in 225 Practice Partner Research Network (PPRNet) Practices in 42 states: July 2010-July 2012 (ICPSR 34554) RSS

Principal Investigator(s):

Summary:

Overview

Through electronic data collection and improving the efficiency of existing data processes to allow both more complete and specific identification of chronic illness, the study objectives included:

  1. Greatly enhance the scope of existing algorithms to permit comprehensive identification of the 20 chronic conditions key to primary care.
  2. Improve the specificity of the existing algorithms to permit more precise automated identification of chronic conditions, limiting the amount of human review required.
  3. Revise the algorithms to permit identification of more than one condition in a text string.

The investigators developed advanced SAS text string search algorithms and developed a modified parsing table that included inclusion and exclusion patterns and resultant diagnoses. The automation searches through each input text string for the inclusion pattern that is not equivalent to the exclusion pattern and maps the string to the corresponding resultant diagnosis. This technique allows the search functions to be easily modified to include additional search criteria and scaled to encompass additional conditions.

Data Dictionary

A data dictionary for 24 chronic conditions was developed. The dictionary assigns ICD-9 diagnosis codes to problem list text in electronic health record data. The dictionary contains 78,458 records and exists in two forms, a Microsoft Access database and a SAS 9.2 dataset. The Microsoft Access database contains 24 tables, one for each condition. The SAS 9.2 dataset contains four fields. The 24 chronic conditions for which problem list text data were examined and assigned to ICD-9 codes. Conditions include Alcohol Use Disorder, Asthma and Allergic Rhinitis, Atherosclerosis, Atrial Fibrillation, Cerebrovascular Disease, Chronic Liver Disease, COPD, Chronic Renal Disease, Coronary Disease, Dementia, Depression, Diabetes Mellitus, Epilepsy, GERD, Heart Failure, Hyperlipidemia, Hypertension, Migraine Headache, Obesity, Osteoarthritis, Osteopenia/Osteoporosis, Parkinson's Disease, Peptic Ulcer Disease and Rheumatoid Arthritis.

Data Access

The data dictionary is not available from ICPSR. For use arrangements, please contact Ruth G. Jenkins, PhD (jenkinsr@musc.edu) or Steven M. Ornstein, MD (ornstesm@musc.edu) at the Practice Partner Research Network (PPRNet), Medical University of South Carolina.

Access Notes

  • One or more files in this study are not available for download due to special restrictions; consult the restrictions note to learn more.

    The data dictionary is not available from ICPSR. For use arrangements, please contact Ruth G. Jenkins, PhD (jenkinsr@musc.edu) or Steven M. Ornstein, MD (ornstesm@musc.edu) at the Practice Partner Research Network (PPRNet), Medical University of South Carolina.

Dataset(s)

Dataset
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No downloadable data files available.

Study Description

Citation

Ornstein, Steven M. Enhancing Analytic Abilities to Identify Complex Patients in 225 Practice Partner Research Network (PPRNet) Practices in 42 states: July 2010-July 2012. ICPSR34554-v1. Charleston, SC: Practice Partner Research Network. Medical University of South Carolina./Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributors], 2013-03-11. http://doi.org/10.3886/ICPSR34554.v1

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Funding

This study was funded by:

  • United States Department of Health and Human Services. Agency for Healthcare Research and Quality (RH 24HS19448-01)

Scope of Study

Subject Terms:   chronic illnesses, health, health care, medical care, medical records, patient care, preventive medicine, primary care

Geographic Coverage:   United States

Time Period:  

  • 2010-07-01--2012-07-01

Date of Collection:  

  • 2010-07-01--2012-07-01

Unit of Observation:   Diagnosis text string

Universe:   All patient diagnosis data in the active PPRNet practices (approximately 225 practices in 42 states) during the study period.

Data Types:   administrative records data, clinical data, machine-readable text

Data Collection Notes:

Other contributors to the study:

  • Ruth G. Jenkins, PhD
  • Jodi Riley
  • Vanessa Congdon

Methodology

Data Source:

Anonymized electronic health records (EHR) data.

Version(s)

Original ICPSR Release:  

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