Identifying Practice Facilitation Delays and Barriers in Primary Care Quality Improvement

Ye, Jiancheng; Zhang, Renwen; Bannon, Jennifer; Wang, Ann A.; Walunas, Theresa L.; Kho, Abel N.; Soulakis, Nicholas D.

doi:10.3122/jabfm.2020.05.200058

Cited by 40 publications

(44 citation statements)

References 39 publications

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“…The avalanche of healthcare data is accelerating precision and personalized medicine. Artificial intelligence and algorithm-based approaches are becoming more and more vital to support clinical decision-making, health care delivery and health services [ 16 – 18 ]. These methods are able to augment health care providers by taking away some of their routine work and enabling them to focus on critical issues [ 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

Predicting mortality in critically ill patients with diabetes using machine learning and clinical notes

Liang

Shen

et al. 2020

BMC Med Inform Decis Mak

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Background Diabetes mellitus is a prevalent metabolic disease characterized by chronic hyperglycemia. The avalanche of healthcare data is accelerating precision and personalized medicine. Artificial intelligence and algorithm-based approaches are becoming more and more vital to support clinical decision-making. These methods are able to augment health care providers by taking away some of their routine work and enabling them to focus on critical issues. However, few studies have used predictive modeling to uncover associations between comorbidities in ICU patients and diabetes. This study aimed to use Unified Medical Language System (UMLS) resources, involving machine learning and natural language processing (NLP) approaches to predict the risk of mortality. Methods We conducted a secondary analysis of Medical Information Mart for Intensive Care III (MIMIC-III) data. Different machine learning modeling and NLP approaches were applied. Domain knowledge in health care is built on the dictionaries created by experts who defined the clinical terminologies such as medications or clinical symptoms. This knowledge is valuable to identify information from text notes that assert a certain disease. Knowledge-guided models can automatically extract knowledge from clinical notes or biomedical literature that contains conceptual entities and relationships among these various concepts. Mortality classification was based on the combination of knowledge-guided features and rules. UMLS entity embedding and convolutional neural network (CNN) with word embeddings were applied. Concept Unique Identifiers (CUIs) with entity embeddings were utilized to build clinical text representations. Results The best configuration of the employed machine learning models yielded a competitive AUC of 0.97. Machine learning models along with NLP of clinical notes are promising to assist health care providers to predict the risk of mortality of critically ill patients. Conclusion UMLS resources and clinical notes are powerful and important tools to predict mortality in diabetic patients in the critical care setting. The knowledge-guided CNN model is effective (AUC = 0.97) for learning hidden features.

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Section: Introductionmentioning

confidence: 99%

Predicting mortality in critically ill patients with diabetes using machine learning and clinical notes

Liang

Shen

et al. 2020

BMC Med Inform Decis Mak

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“…24 Hospital HIS can contribute to locally-grounded information that has been utilized to develop national or regional policies and make effective decisions for health-related resource allocation and responses. 25 By using emerging technology, Chinese hospital HIS optimized the process of diagnosis and treatment, improved the efficiency of diagnosis and treatment, and facilitated the timely treatment of patients. The construction of intelligent hospitals and internet hospitals improved health care services, such as the appointment of diagnosis and treatment, which played an active role in responding to the COVID-19 pandemic.…”

Section: Discussionmentioning

confidence: 99%

Health Information System's Responses to COVID-19 Pandemic in China: A National Cross-sectional Study

2021

Appl Clin Inform

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Objective After the outbreak of the coronavirus disease 2019 (COVID-19) pandemic, Chinese hospitals and health information technology (HIT) vendors collaborated to provide comprehensive information technology support for pandemic prevention and control. This study aims to describe the responses from the health information systems (HIS) to the COVID-19 pandemic and provide empirical evidence in the application of emerging health technologies in China. Methods This observational descriptive study utilized a nationally representative, cross-sectional survey of hospitals in China (N = 1,014) from 30 provincial administrative regions across the country. Participants include hospital managers, hospital information workers, and health care providers. Results Among all the responses, the most popular interventions and applications include expert question-and-answer sessions and science popularization (61.74%) in online medical consultation, online appointment registration (58.97%) in online medical service, and remote consultation (75.15%) in telehealth service. A total of 63.71% of the participating hospitals expanded their fever clinics during the pandemic, 15.38% hospitals used new or upgraded mobile ward rounds systems, and 44.68% hospitals applied online self-service systems. Challenges and barriers include protecting network information security (57.00%) since some hospitals experienced cybersecurity incidents. 71.79% participants hope to shorten wait time and optimize the treatment process. Health care workers experienced increased amount of work during the pandemic, while hospital information departments did not experience significant changes in their workload. Conclusion In the process of fighting against the COVID-19, hospitals have widely used traditional and emerging novel HITs. These technologies have strengthened the capacity of prevention and control of the pandemic and provided comprehensive information technology support while also improving accessibility and efficiency of health care delivery.

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“…Within H3, we created a data collection tool to track the day-to-day activities of practice facilitators as they worked with practices and the study interventions the practices implemented. Practice facilitators recorded their activities with the practices in standardized fields using the Facilitation ACtivity and Intervention Tracking System (FACITS) tool [31], a modified customer relationship management system built using the QuickBase platform [32] to document recruitment and facilitation. FACITS was designed in collaboration with the practice facilitation team for H3 with an emphasis on collecting essential data elements for study evaluation and optimizing documentation efficiency.…”

Section: Tracking H3 Practice Facilitation Activities and Qi Intervenmentioning

confidence: 99%

Does coaching matter? Examining the impact of specific practice facilitation strategies on implementation of quality improvement interventions in the Healthy Hearts in the Heartland study

Walunas

Bannon

et al. 2021

Implementation Sci

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Background Practice facilitation is a multicomponent implementation strategy used to improve the capacity for practices to address care quality and implementation gaps. We sought to assess whether practice facilitators use of coaching strategies aimed at improving self-sufficiency were associated with improved implementation of quality improvement (QI) interventions in the Healthy Hearts in the Heartland Study. Methods We mapped 27 practice facilitation activities to a framework that classifies practice facilitation strategies by the degree to which the practice develops its own process expertise (Doing Tasks, Project Management, Consulting, Teaching, and Coaching) and then used regression tree analysis to group practices by facilitation strategies experienced. Kruskal-Wallis tests were used to assess whether practice groups identified by regression tree analysis were associated with successful implementation of QI interventions and practice and study context variables. Results There was no association between number of strategies performed by practice facilitators and number of QI interventions implemented. Regression tree analysis identified 4 distinct practice groups based on the number of Project Management and Coaching strategies performed. The median number of interventions increased across the groups. Practices receiving > 4 project management and > 6 coaching activities implemented a median of 17 of 35 interventions. Groups did not differ significantly by practice size, association with a healthcare network, or practice type. Statistically significant differences in practice location, number and duration of facilitator visits, and early study termination emerged among the groups, compared to the overall practice population. Conclusions Practices that engage in more coaching-based strategies with practice facilitators are more likely to implement more QI interventions, and practice receptivity to these strategies was not dependent on basic practice demographics.

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Identifying Practice Facilitation Delays and Barriers in Primary Care Quality Improvement

Cited by 40 publications

References 39 publications

Predicting mortality in critically ill patients with diabetes using machine learning and clinical notes

Predicting mortality in critically ill patients with diabetes using machine learning and clinical notes

Health Information System's Responses to COVID-19 Pandemic in China: A National Cross-sectional Study

Does coaching matter? Examining the impact of specific practice facilitation strategies on implementation of quality improvement interventions in the Healthy Hearts in the Heartland study

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