BACKGROUND: In order to proactively manage congestive heart failure (CHF) patients, an effective CHF case finding algorithm is required to process both structured and unstructured electronic medical records (EMR) to allow complementary and cost-efficient identification of CHF patients. METHODS AND RESULTS: We set to identify CHF cases from both EMR codified and natural language processing (NLP) found cases. Using narrative clinical notes from all Maine Health Information Exchange (HIE) patients, the NLP case finding algorithm was retrospectively (July 1, 2012-June 30, 2013) developed with a random subset of HIE associated facilities, and blind-tested with the remaining facilities. The NLP based method was integrated into a live HIE population exploration system and validated prospectively (July 1, 2013-June 30, 2014). Total of 18,295 codified CHF patients were included in Maine HIE. Among the 253,803 subjects without CHF codings, our case finding algorithm prospectively identified 2411 uncodified CHF cases. The positive predictive value (PPV) is 0.914, and 70.1% of these 2411 cases were found to be with CHF histories in the clinical notes. CONCLUSIONS: A CHF case finding algorithm was developed, tested and prospectively validated. The successful integration of the CHF case findings algorithm into the Maine HIE live system is expected to improve the Maine CHF care.
BACKGROUND: In order to proactively manage congestive heart failure (CHF) patients, an effective CHF case finding algorithm is required to process both structured and unstructured electronic medical records (EMR) to allow complementary and cost-efficient identification of CHFpatients. METHODS AND RESULTS: We set to identify CHF cases from both EMR codified and natural language processing (NLP) found cases. Using narrative clinical notes from all Maine Health Information Exchange (HIE) patients, the NLP case finding algorithm was retrospectively (July 1, 2012-June 30, 2013) developed with a random subset of HIE associated facilities, and blind-tested with the remaining facilities. The NLP based method was integrated into a live HIE population exploration system and validated prospectively (July 1, 2013-June 30, 2014). Total of 18,295 codified CHFpatients were included in Maine HIE. Among the 253,803 subjects without CHF codings, our case finding algorithm prospectively identified 2411 uncodified CHF cases. The positive predictive value (PPV) is 0.914, and 70.1% of these 2411 cases were found to be with CHF histories in the clinical notes. CONCLUSIONS: A CHF case finding algorithm was developed, tested and prospectively validated. The successful integration of the CHF case findings algorithm into the Maine HIE live system is expected to improve the Maine CHF care.
Authors: Sungrim Moon; Sijia Liu; Christopher G Scott; Sujith Samudrala; Mohamed M Abidian; Jeffrey B Geske; Peter A Noseworthy; Jane L Shellum; Rajeev Chaudhry; Steve R Ommen; Rick A Nishimura; Hongfang Liu; Adelaide M Arruda-Olson Journal: Int J Med Inform Date: 2019-05-13 Impact factor: 4.046
Authors: Le Zheng; Yue Wang; Shiying Hao; Andrew Y Shin; Bo Jin; Anh D Ngo; Medina S Jackson-Browne; Daniel J Feller; Tianyun Fu; Karena Zhang; Xin Zhou; Chunqing Zhu; Dorothy Dai; Yunxian Yu; Gang Zheng; Yu-Ming Li; Doff B McElhinney; Devore S Culver; Shaun T Alfreds; Frank Stearns; Karl G Sylvester; Eric Widen; Xuefeng Bruce Ling Journal: JMIR Med Inform Date: 2016-11-11
Authors: Carlos Luis Sanchez Bocanegra; Jose Luis Sevillano Ramos; Carlos Rizo; Anton Civit; Luis Fernandez-Luque Journal: BMC Med Inform Decis Mak Date: 2017-05-15 Impact factor: 2.796
Authors: Shiying Hao; Yue Wang; Bo Jin; Andrew Young Shin; Chunqing Zhu; Min Huang; Le Zheng; Jin Luo; Zhongkai Hu; Changlin Fu; Dorothy Dai; Yicheng Wang; Devore S Culver; Shaun T Alfreds; Todd Rogow; Frank Stearns; Karl G Sylvester; Eric Widen; Xuefeng B Ling Journal: PLoS One Date: 2015-10-08 Impact factor: 3.240