J Clin Med Res

Journal of Clinical Medicine Research, ISSN 1918-3003 print, 1918-3011 online, Open Access

Article copyright, the authors; Journal compilation copyright, J Clin Med Res and Elmer Press Inc

Journal website http://www.jocmr.org

Short Communication

Volume 11, Number 6, June 2019, pages 458-463

Phenotyping to Facilitate Accrual for a Cardiovascular Intervention

Figure 1. DP consists of multiple screen cycles. At the start of each cycle, phenotyping is used to identify eligible patients from the EHR, creating an ordered list in which the most eligible patients are listed first. This list is manually screened, and the results are analyzed to improve phenotyping for the next cycle. DP: dynamic phenotyping; EHR: electronic health record.

Figure 2. Composition of manual screening performed in each cycle. 1) No HF and EF accounted for 25% of false positives. HF was detected by machine learning and EF was extracted using a simple regular expression from clinical notes. 2) Optimization of EF parser and inferring HF from low EF eliminated many false positives. But failure to exclude patients not primarily managed by the outpatient cardiology clinic at BWH emerged as a challenge. This was because the cardiologist was inferred using total number of EHRs entries authored for the patient. 3) Cardiologist was inferred from number of EHR entries limited to the outpatient setting. But this did not significantly reduce false positives. 4) Use of machine learning to infer the primary cardiologist significantly excluded patients that are not managed at BWH. HF: heart failure; EF: ejection fraction; BWH: Brigham and Women’s Hospital; EHR: electronic health record.

Inclusion criteria	Model types	Data modalities	Cycle
Inclusion criteria	Model types	Data modalities	I	II	III	IV
EF: ejection fraction.
Heart failure	Logistic regression	Coded data and notes	√
	Simple inference	EF algorithm		√	√	√
EF ≤ 40	Simple regular expression	All notes	√
	Elaborate text-processing	Notes from cardiology service		√	√	√
Primary cardiologist at institution	Rules	Notes	√
	Rules	Coded data and notes		√	√
	Logistic regression	Coded data and notes				√

Cycle	I	II	III	IV	Total
PSR: positive screening rate.
Screened in (A)	165	24	14	30	233
Screened out (B)	670	26	34	59	789
Total screened (C = A + B)	835	50	48	89	1,022
PSR (A/C)%	20	48	29	34	23