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Mining Clinicians' Electronic Documentation to Identify Heart Failure Patients with Ineffective Self-Management: A
Maxim Topaz1, Kavita Radhakrishnan2, Victor Lei3
1Harvard Medical School, Boston, MA, USA.
Studies in Health Technology and Informatics
|June 23, 2016
Summary
Identifying heart failure patients with poor self-management is crucial. Text-mining clinical notes shows 38.2% of patients struggle with diet, appointments, or medication adherence.
Area of Science:
- Cardiology
- Medical Informatics
- Health Services Research
Background:
- Effective self-management significantly reduces heart failure hospitalizations by up to 50%.
- Patient self-management in heart failure remains a persistent challenge.
- Identifying patients with self-management deficits is critical for intervention.
Purpose of the Study:
- To explore the feasibility of using text-mining to identify heart failure patients with ineffective self-management.
- To develop and apply a self-management vocabulary for clinical note analysis.
Main Methods:
- A comprehensive self-management vocabulary was created from literature and clinical notes.
- A regular expression search was performed on 43,107 clinical notes from 545 heart failure patients.
- Patients were treated within Partners Healthcare hospitals in Boston, MA, USA.
Main Results:
- 38.2% (n=208) of heart failure patients exhibited documented ineffective self-management.
- Key areas of poor adherence included diet (28.4%), missed medical encounters (26.4%), and medication (20.2%).
- A significant portion (34.6%) had non-specified self-management issues like 'compliance issues'.
Conclusions:
- Text-mining demonstrates feasibility for identifying heart failure patients with ineffective self-management.
- This approach can highlight specific self-management deficits.
- Further development of natural language processing algorithms is needed to assist clinicians.
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