Innovative Informatics Approaches for Peripheral Artery Disease: Current State and Provider Survey of Strategies for Improving Guideline-Based Care

Abstract

Objective To quantify compliance with guideline recommendations for secondary prevention in peripheral artery disease (PAD) using natural language processing (NLP) tools deployed to an electronic health record (EHR) and investigate provider opinions regarding clinical decision support (CDS) to promote improved implementation of these strategies. Patients and Methods Natural language processing was used for automated identification of moderate to severe PAD cases from narrative clinical notes of an EHR of patients seen in consultation from May 13, 2015, to July 27, 2015. Guideline-recommended strategies assessed within 6 months of PAD diagnosis included therapy with statins, antiplatelet agents, angiotensin-converting enzyme inhibitors or angiotensin receptor blockers, and smoking abstention. Subsequently, a provider survey was used to assess provider knowledge regarding PAD clinical practice guidelines, comfort in recommending secondary prevention strategies, and potential role for CDS. Results Among 73 moderate to severe PAD cases identified by NLP, only 12 (16%) were on 4 guideline-recommended strategies. A total of 207 of 760 (27%) providers responded to the survey; of these 141 (68%) were generalists and 66 (32%) were specialists. Although 183 providers (88%) managed patients with PAD, 51 (25%) indicated they were uncomfortable doing so; 138 providers (67%) favored the development of a CDS system tailored for their practice and 146 (71%) agreed that an automated EHR-derived mortality risk score calculator for patients with PAD would be helpful. Conclusion Natural language processing tools can identify cases from EHRs to support quality metric studies. Findings of this pilot study demonstrate gaps in application of guideline-recommended strategies for secondary risk prevention for patients with moderate to severe PAD. Providers strongly support the development of CDS systems tailored to assist them in providing evidence-based care to patients with PAD at the point of care.

Provider Survey on Automated Clinical Decision Support for Cardiovascular Risk Assessment

“…In comparison to other similar studies, the provider survey response rate of 35.8% (100 of 279) was greater than the response rate of 27% (207 of 760) reported by Chaudhry et al 14 investigating provider opinions about CDS for provision of evidence-based care to patients with peripheral artery disease at the point-of-care. Additionally, the response rate of the study herein was greater than the reported rates of 10% (10 of 100) to 15% (15 of 100) from a random sample of physicians across specialties (primary care, obstetrics/gynecology, and cardiology) in a national survey of guideline-recommended strategies for cardiovascular disease prevention.…”

Application of Artificial Intelligence in Community-Based Primary Health Care: Systematic Scoping Review and Critical Appraisal

“…The majority of the 90 included studies (68/90, 76%) investigated the use of AI in relation to a specific medical condition. Conditions studied were vascular diseases including hypertension, hypercholesteremia, peripheral arterial disease, and congestive heart failure (10/90, 11%) [ 40 - 49 ]; infectious diseases including influenza, herpes zoster, tuberculosis, urinary tract infections, and subcutaneous infections (8/90, 9%) [ 50 - 57 ]; type 2 diabetes (5/90, 6%) [ 58 - 62 ]; respiratory disorders including chronic obstructive pulmonary disease and asthma (6/90, 8%) [ 63 - 69 ]; orthopedic disorders including rheumatoid arthritis, gout, and lower back pain (5/90, 5%) [ 36 , 39 , 70 - 72 ]; neurological disorders including stroke, Parkinson disease, Alzheimer disease [ 73 - 75 ], and cognitive impairments (6/90, 5%) [ 76 , 77 ]; cancer including colorectal cancer, and head and neck cancer (4/90, 4%) [ 78 - 81 ]; psychological disorders including depression and schizophrenia (3/90, 3%) [ 82 - 84 ]; diabetic retinopathy (3/90, 3%) [ 85 - 87 ]; suicidal ideations (2/90, 2%) [ 88 , 89 ]; tropical diseases including malaria (2/90, 2%) [ 90 , 91 ]; renal disorders (2/90, 2%) [ 92 , 93 ]; autism spectrum disorder (2/90, 2%) [ 94 , 95 ]; venous disorders including deep vein thrombosis and venous ulcers (2/90, 2%) [ 96 , 97 ]; and other health conditions (8/90, 8%) [ 98 - 105 ].…”

Feature Extraction and Analysis of Natural Language Processing for Deep Learning English Language

“…The idea of understanding the segmentation first is to solve the lack of global information in the traditional matching segmentation, while the statistical method lacks the structural information of the sentence [36]- [39]. Relevant scholars use deep learning to perform sequence labeling in the NLP field [40], [41]. It can also add a sequence labeling model to combine with the output of the previous neural network to extract the best labeling sequence through the Viterbi algorithm.…”