Electronic health record data extraction: Physical therapists’ documentation of physical activity assessments and prescriptions for patients with chronic low back pain

ABSTRACT

Background

Despite physical activity being a major component of managing chronic low back pain, < 50% of patients receive physical activity interventions. Electronic Health Records can deepen our understanding about this clinical gap.

Objective

We aimed to: 1) develop and test a data abstraction form that captures physical activity documentation; and 2) explore physical therapists’ documentation of physical activity assessments and interventions.

Methods

We developed a data abstraction form using previously published practice guidelines. After identifying the forms’ inter-rater reliability, we used it to explore physical therapists’ documentation related to physical activity assessments and interventions for patients with chronic low back pain.

Results

The final data abstraction form included information about physical activity history, assessments, interventions, general movement discussion, and plan. Our inter-rater reliability was high. Of the 18 patients, 66.7% had documentation about their PA history. Across the 56 encounters, 14 (25.0%) included an assessment, 18 (32.1%) an intervention, 18 (32.1%) a general movement discussion, and 12 (21.4%) included a plan.

Conclusion

Using our reliable data abstraction form we identified a lack of documentation about physical activity assessments and interventions among patients with chronic low back pain. A larger study is needed to examine the generalizability of these results.

KEYWORDS:

• Documentation
• physical activity
• chronic low back pain
• best practice
• physical therapy

Acknowledgments

We sincerely thank and acknowledge the following individuals for their contribution to this manuscript: Dr. Sharon Henry, PT, PhD (Department of Rehabilitation and Movement Science, University of Vermont, Burlington, Vermont) for her willingness to share data from her clinical trial; and Jonathan P. Bona, PhD (Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, Arkansas) for his willingness to provide expert knowledge on Natural Language Processing.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the National Institutes of Health (NIH, K76AG074920 to JLV), the Translational Research Institute (TRI) UL1 TR003107 (UAMS) through the National Center for Advancing Translational Sciences (NCATS) of the NIH and the Center on Health Services Training and Research (CoHSTAR) funded by the Foundation for Physical Therapy Research in partnership with the American Physical Therapy Association. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.