Leveraging Artificial Intelligence to Improve Provider Documentation in Patient Medical Records

Clinical documentation is at the center of a patient\u27s medical record; this record contains all the information applicable to the care a patient receives in the hospital. The practice problem addressed in this project was the lack of clear, consistent, accurate, and complete patient medical records in a pediatric hospital. Although the occurrence of incomplete medical records has been a known issue for the project hospital, the issue was further intensified following the implementation of the 10th revision of International Classification of Diseases (ICD-10) standard for documentation, which resulted in gaps in provider documentation that needed to be filled. Based on this, the researcher recommended a quality improvement project and worked with a multidisciplinary team from the hospital to develop an evidence-based documentation guideline that incorporated ICD-10 standard for documenting pediatric diagnoses. Using data generated from the guideline, an artificial intelligence (AI) was developed in the form of best practice advisory alerts to engage providers at the point of documentation as well as augment provider efforts. Rosswurm and Larrabee\u27s conceptual framework and Kotter\u27s 8-step change model was used to develop the guideline and design the project. A descriptive data analysis using sample T-test significance indicated that financial reimbursement decreased by 25%, while case denials increased by 28% after ICD-10 implementation. This project promotes positive social change by improving safety, quality, and accountability at the project hospital

Bronchial thermoplasty: implementing best practice in the era of cost containment.

Increasing dependence on advanced technologies in the 21st century has created a dilemma between the practice and business of medicine. From information technology to robotic surgery, new technologies have expanded treatment possibilities and have potentially improved patient outcomes and safety. Simultaneously, their escalating costs limit access for certain patients and health care facilities. Nevertheless, medical decisions should not simply be based on cost. Input from physicians and other health care specialists as well as adherence to best practice position statements, are vital to implementing truly cost-effective strategies in medicine. Bronchial thermoplasty (BT), a US Food and Drug Administration approved bronchoscopy procedure in difficult-to-control persistent asthma, is a prime example of a new technology facing cost and implementation challenges. We discuss the specific indications and contraindications for BT and review recent real-world experiences that can provide the foundation for building a comprehensive asthma program that provides BT for difficult-to-control asthma patients who fail national guideline treatment recommendations after an adequate clinical trial of one. We also offer insight into the barriers to implementing a successful BT program and strategies for overcoming them

A Quality Improvement Project on Diagnosis and Management of Asthma in a Private Pediatric Setting.

Abstract Background: Although national guidelines exist for the diagnosis and management of asthma, general practice differs significantly from recommendations. Quality improvement methodology when implemented can narrow quality gaps. Objective: The objective of the project was to create and implement a plan of action to address identified gaps in key clinical activities of asthma care among pediatric population in a private pediatric setting in Northern California Methods: The project was centered on the use of Education in Quality Improvement for Pediatric Practice (EQIPP), a program of the American Academy of the Pediatrics. Both the pediatrician and the DNP student took this course and employed its methods to improve asthma management. EQIPP supports providers in improving their practice with didactic materials that help participants develop quality improvement project and tools to evaluate the outcomes of that project. Results: Based on the asthmatic patient data analysis the quality improvement team identified that the clinic lacks compliance in the following areas of national guidelines. a) Diagnosis of asthma, b) Asthma action plan and c) Asthma control and follow up. The team then developed and implemented an improvement plan based on EQIPP. Conclusion: The quality improvement project enriched the pediatric practice management of asthma patients and similar projects could be implemented in other settings too. Keywords: Asthma, EQIPP, guidelines, pediatric practice, diagnosis, asthma action plan, contro

Leveraging AI and Machine Learning to Develop and Evaluate a Contextualized User-Friendly Cough Audio Classifier for Detecting Respiratory Diseases: Protocol for a Diagnostic Study in Rural Tanzania

Background: Respiratory diseases, including active tuberculosis (TB), asthma, and chronic obstructive pulmonary disease (COPD), constitute substantial global health challenges, necessitating timely and accurate diagnosis for effective treatment and management. Objective: This research seeks to develop and evaluate a noninvasive user-friendly artificial intelligence (AI)–powered cough audio classifier for detecting these respiratory conditions in rural Tanzania. Methods: This is a nonexperimental cross-sectional research with the primary objective of collection and analysis of cough sounds from patients with active TB, asthma, and COPD in outpatient clinics to generate and evaluate a noninvasive cough audio classifier. Specialized cough sound recording devices, designed to be nonintrusive and user-friendly, will facilitate the collection of diverse cough sound samples from patients attending outpatient clinics in 20 health care facilities in the Shinyanga region. The collected cough sound data will undergo rigorous analysis, using advanced AI signal processing and machine learning techniques. By comparing acoustic features and patterns associated with TB, asthma, and COPD, a robust algorithm capable of automated disease discrimination will be generated facilitating the development of a smartphone-based cough sound classifier. The classifier will be evaluated against the calculated reference standards including clinical assessments, sputum smear, GeneXpert, chest x-ray, culture and sensitivity, spirometry and peak expiratory flow, and sensitivity and predictive values. Results: This research represents a vital step toward enhancing the diagnostic capabilities available in outpatient clinics, with the potential to revolutionize the field of respiratory disease diagnosis. Findings from the 4 phases of the study will be presented as descriptions supported by relevant images, tables, and figures. The anticipated outcome of this research is the creation of a reliable, noninvasive diagnostic cough classifier that empowers health care professionals and patients themselves to identify and differentiate these respiratory diseases based on cough sound patterns. Conclusions: Cough sound classifiers use advanced technology for early detection and management of respiratory conditions, offering a less invasive and more efficient alternative to traditional diagnostics. This technology promises to ease public health burdens, improve patient outcomes, and enhance health care access in under-resourced areas, potentially transforming respiratory disease management globally

How Implementing a Digital Competency Management System Reduced Nurse Training Cost and Improved NPD Practitioner Satisfaction in a Pediatric Hospital

Healthcare organizations must have high-quality nursing staff to deliver optimal patient care. Educators and managers evaluate nurses by their performance through nursing competencies, or “knowledge, skills, [and] abilities” (KSA) (American Nurses Association, 2015, p. 86). Traditional competency evaluations and manual tracking posed a problem within one pediatric hospital. Leaders did not have a transparent way to see the knowledge and skills of their nursing staff. This resulted in increased organizational costs due to retraining and increased workload and job dissatisfaction among educators. The purpose of this quality improvement (QI) project was to evaluate how implementing a digital competency management system (CMS) affected nurse training costs and assess nursing professional development (NPD) practitioners’ satisfaction after the digital CMS conversion. Technology Acceptance Model (TAM) was used to guide the QI project. The student conducted a cost analysis and measured nurse training cost prior to and after implementing a digital competency management system. The student also administered pre- and post-survey evaluations to determine NPD practitioners’ satisfaction before and after digital implementation. Retrospective data of training costs were collected prior to implementing the CMS. A Wilcoxon signed rank test compared the medians to examine the pre and post survey results of NPD practitioners’ satisfaction scores. The quality improvement project demonstrated that a digital CMS reduced nurse training costs by more than a half a million dollars and increased NPD practitioners’ satisfaction

Surveillance of asthma control in an urban Pediatric Primary Care Center

INTRODUCTION: Asthma is the most prevalent chronic disease in children, disproportionately affecting children from racial or ethnic minority groups and low-income families. Boston Medical Center’s Pediatric Primary Care Center serves these patient populations predominantly from the surrounding neighborhoods. It has been found that there are gaps in asthma care including diagnosing asthma in infants and young children, under-prescribing of preventive medication in all age groups, and variable management of children with poorly controlled asthma. In alignment with the accountable care organization model, health care professionals at BMC are using evidence-based care and population-based approaches to reduce asthma morbidity and thus improve the quality of life for patients with asthma and their families. METHODS: A quality improvement initiative was conducted at BMC’s Pediatric Primary Care Center. The aim was to develop routine surveillance of asthma control for the clinic population in order to identify and intervene on patients who have poorly controlled asthma. The Asthma Control Test (ACT) and the Test for Respiratory and Asthma Control in Kids (TRACK) were adapted into practice as validated patient-parent-reported tools to use to assess asthma control at all primary care office visits. Process measure included the percentage of visits with a documented asthma control testing in the electronic medical record. Outcome measures included (1) percentage of patients with poorly controlled asthma presenting to the clinic, as indicated by low ACT/TRACK scores, and (2) percentage of visits with a documented provider action in response to low ACT/TRACK scores. Iterative Plan, Do, Study, Act (PDSA) cycles optimized results; process and outcome measures were analyzed on run charts for trends. RESULTS AND CONCLUSIONS: Patient-centered strategies for visits and population-based systems to analyze outcomes are effective at delivering quality care for BMC’s pediatric asthma patient population. Following the implementation of routine asthma control screening in primary care, the percentage of visits with documented ACT/TRACK scores went from a baseline of 8% to 86%. Week to week variation was mostly attributed to higher patient visit volume beginning in the Fall season when epidemiologically there is a substantially increased frequency of asthma exacerbations in children. A median of 23% of patients report poorly controlled asthma during their visit. The percent of visits with documented provider action increased from 87% to 95% during this quality improvement initiative, indicating that patients were receiving targeted care needs including medication management and asthma education in response to low ACT/TRACK scores. However, consistent and timely delivery of preventive care services continues to be a challenge, particularly for a clinic serving high-risk, underserved, and culturally diverse patient populations.2020-06-17T00:00:00

Practice Characteristics That Matter In the Provision of Health Education Services By Primary Care Physicians

The onset of chronic disease is occurring earlier and more often in the lives of adult citizens of the United States. The literature has effectively demonstrated the efficacy that physician provision of health education services offers their patients and the positive effects it has for lowering risk factors for chronic disease. The literature has described the complexities physicians encounter in providing these services. The literature is not as plentiful in defining and describing the characteristics of physician practice that are associated with increased health education provision. This study is an analysis of the factors that are associated with provision of health education by primary care physicians in their offices. For this study, three years of the National Ambulatory Care Medical Care Survey, (NAMCS) are used for analysis. Selected factors germane to physician practice are analyzed for their effects on three risk factors for chronic disease; tobacco use, lack of exercise and obesity. The study findings show that use of electronic health record systems are associated with increased odds of providing health education services over non automated physician practices. Physicians of private group practices offer health education services less often than physicians in federally qualified health centers. Use of e mail, telephone conferences, and whether the physician received Allopathic or osteopathic training was not associated with provision of health education. The study is relevant because of the need for a re-engineering of the financial and structural systems of physician practice that pre-empt offering health education in physician practice. Factors identified in this study, should be important considerations in the design of a new physician payment system that will incentivize physicians to include evidenced based health education as essential component of primary care delivery

Master of Science

thesisAsthma, diabetes, and depression are chronic diseases managed through the Primary Care Clinical Program at Intermountain Healthcare. Primary Care Providers (PCPs) receive monthly reports on their patients with these conditions. The reporting paradigm focuses on individual diseases. PCPs have asked for a consolidated view of chronic disease, one that is patient-centric rather than disease-centric. A clinical decision support tool was developed using data from Intermountain's enterprise data warehouse. A cube was built to report on asthma, diabetes, and depression patients simultaneously. 183, 000 patients were included in the study. The tool measures PCP's adherence to best practices for chronic disease management. It also allows ad-hoc analysis of large data sets as well as actionable reports for PCPs to identify gaps in adherence to best practices. Primary care providers can view their patient populations with asthma, diabetes and depression in a consolidated report. The decision support tool was successfully built as a prototype for chronic disease management. The tool has the potential to scale and include many chronic conditions for reporting. It was demonstrated to executives, directors, and PCPs at Intermountain. Chronic disease management should be done with a patient focus rather than a disease focus. Information technology has an important role to play in the support of iv primary care and the medical home. Clinical decision support tools can be built to improve population-level and patient-level chronic disease management

Approaches to enhance interpretability and meaningful use of big data in population health practice and research

While many public health and medical studies use big data, the potential for big data to further population health has yet to be fully realized. Because of the complexities associated with the storage, processing, analysis, and interpretation of these data, few research findings from big data have been translated into practice. Using small area estimation synthetic data and electronic health record (EHR) data, the overall goal of this dissertation research was to characterize health-related exposures with an explicit focus on meaningful data interpretability. In our first aim, we used regression models linked to population microdata to respond to high-priority needs articulated by our community partners in New Bedford, MA. We identified census tracts with an elevated percentage of high-risk subpopulations (e.g., lower rates of exercise, higher rates of diabetes), information our community partners used to prioritize funding opportunities and intervention programs. In our second and third aims, we scrutinized EHR data on children seen at Boston Medical Center (Boston, MA), New England’s largest safety-net hospital, from 2013 through 2017 and uncovered racial/ethnic disparities in asthma severity and residential mobility using logistic regression. We built upon a validated asthma computable phenotype to create a computable phenotype for asthma severity that is based in clinical asthma guidelines. We found that children for whom severity could be ascertained from these EHR data were less likely to be Hispanic and that Black children were less likely to have lung function testing data present. Lastly, we constructed contextualized residential mobility and immobility metrics using EHR address data and the Child Opportunity Index 2.0, identified opportunities and challenges EHR address data present to study this topic, and found significant racial/ethnic disparities in access to neighborhood opportunity. Our findings highlighted the perpetuation of residence in low opportunity areas among non-White children. The main challenge of this dissertation, to work within the limitations inherent to big data to extract meaningful knowledge from these data and by linking to external datasets, turned out to be an opportunity to engage in solutions-oriented research and do work that, to quote Aristotle, “…is greater than the sum of its parts”. Through strategies ranging from engaging with community partners to examining who and what data are captured (and not captured) in EHR health and address data, this dissertation demonstrated potential ways to leverage big data sources to further public health and health equity