The role of electronic records in the integration of oral health and primary care services in community health centers

BACKGROUND: Medical-Dental integration involves the provision of fluoride varnish application, caries risk assessment, anticipatory guidance, and provision of dental referrals by pediatricians during well-child visits. Integration has been recommended as a means to increase access to quality dental care for patients from racial and ethnic minority groups who are at an increased risk of developing oral health problems. METHODS: Guided by the RE-AIM framework (Reach, Efficacy/Effectiveness, Adoption, Implementation, Maintenance), this case study explored the barriers and facilitators for the incorporation of a medical-dental integration program at two community health centers in Massachusetts. Specifically, this study explored the degree to which electronic records were instrumental in the provision and documentation of oral health preventive services during pediatric primary care at the study sites. Data sources included analysis of records from 2014–2015 (before integration) to those from 2016-2018 (post integration), interviews with staff, clinicians, and administrators and direct observations of the workflow at dental and pediatric medicine departments in the study sites. A General Estimating Equation Analysis was conducted to estimate the odds of application of oral health preventive measures before and after electronic dental and medical electronic records were integrated at one of the sites. FINDINGS: During the years post-record integration, children were 40.3 times more likely to receive dental screenings, 2.7 times more likely to receive fluoride varnish during well child visits and 1.6 times more likely to receive fluoride in the dental department within six months of their well child visits compared to the period prior to integration. Respondents identified the complexity, ease of use and accessibility of tools within the electronic medical records as significant factors in success of integration efforts. CONCLUSIONS: Community health centers interested in successfully implementing a medical-dental integration model should invest in sufficient workflow and training resources for the transition to the new records system, develop a simplified protocol for the application of dental preventive services, design accessible electronic tools for documentation of services, and establish accurate reporting systems for both internal program monitoring and external surveillance purposes

Developing A Fair Individualized Polysocial Risk Score (iPsRS) for Identifying Increased Social Risk of Hospitalizations in Patients with Type 2 Diabetes (T2D)

Background: Racial and ethnic minority groups and individuals facing social disadvantages, which often stem from their social determinants of health (SDoH), bear a disproportionate burden of type 2 diabetes (T2D) and its complications. It is therefore crucial to implement effective social risk management strategies at the point of care. Objective: To develop an EHR-based machine learning (ML) analytical pipeline to identify the unmet social needs associated with hospitalization risk in patients with T2D. Methods: We identified 10,192 T2D patients from the EHR data (from 2012 to 2022) from the University of Florida Health Integrated Data Repository, including contextual SDoH (e.g., neighborhood deprivation) and individual-level SDoH (e.g., housing stability). We developed an electronic health records (EHR)-based machine learning (ML) analytic pipeline, namely individualized polysocial risk score (iPsRS), to identify high social risk associated with hospitalizations in T2D patients, along with explainable AI (XAI) techniques and fairness assessment and optimization. Results: Our iPsRS achieved a C statistic of 0.72 in predicting 1-year hospitalization after fairness optimization across racial-ethnic groups. The iPsRS showed excellent utility for capturing individuals at high hospitalization risk; the actual 1-year hospitalization rate in the top 5% of iPsRS was ~13 times as high as the bottom decile. Conclusion: Our ML pipeline iPsRS can fairly and accurately screen for patients who have increased social risk leading to hospitalization in T2D patients

Designing a Patient-Centered Clinical Workflow to Assess Cyberbully Experiences of Youths in the U.S. Healthcare System

Cyberbullying or online harassment is often defined as when someone repeatedly and intentionally harasses, mistreats, or makes fun of others aiming to scare, anger or shame them using electronic devices [296]. Youths experiencing cyberbullying report higher levels of anxiety and depression, mental distress, suicide thoughts, and substance abuse than their non-bullied peers [360, 605, 261, 354]. Even though bullying is associated with significant health problems, to date, very little youth anti-bullying efforts are initiated and directed in clinical settings. There is presently no standardized procedure or workflow across health systems for systematically assessing cyberbullying or other equally dangerous online activities among vulnerable groups like children or adolescents [599]. Therefore, I developed a series of research projects to link digital indicators of cyberbullying or online harassment to clinical practices by advocating design considerations for a patient-centered clinical assessment and workflow that addresses patients’ needs and expectations to ensure quality care. Through this dissertation, I aim to answer these high-level research questions:RQ1. How does the presence of severe online harassment on online platforms contribute to negative experiences and risky behaviors within vulnerable populations? RQ2. How efficient is the current mechanism of screening these risky online negative experiences and behaviors, specifically related to cyberbully, within at-risk populations like adolescent in clinical settings? RQ3. How might evidence of activities and negative harassing experiences on online platforms best be integrated into electronic health records during clinical treatment? I first explore how harassment is presented within different social media platforms from diverse contexts and cultural norms (study 1,2, and 3); next, by analyzing actual patient data, I address current limitations in the screening process in clinical settings that fail to efficiently address core aspect of cyberbullying and their consequences within adolescent patients (study 4 and 5); finally, connecting all my findings, I recommend specific design guidelines for a refined screening tool and structured processes for implementation and integration of the screened data into patients’ electronic health records (EHRs) for better patient assessment and treatment outcomes around cyberbully within adolescent patients (study 6)

Development and Evaluation of an Interdisciplinary Periodontal Risk Prediction Tool Using a Machine Learning Approach

Periodontitis (PD) is a major public health concern which profoundly affects oral health and concomitantly, general health of the population worldwide. Evidence-based research continues to support association between PD and systemic diseases such as diabetes and hypertension, among others. Notably PD also represents a modifiable risk factor that may reduce the onset and progression of some systemic diseases, including diabetes. Due to lack of oral screening in medical settings, this population does not get flagged with the risk of developing PD. This study sought to develop a PD risk assessment model applicable at clinical point-of-care (POC) by comparing performance of five supervised machine learning (ML) algorithms: Naïve Bayes, Logistic Regression, Support Vector Machine, Artificial Neural Network and Decision Tree, for modeling risk by retrospectively interrogating clinical data collected across seven different models of care (MOC) within the interdisciplinary settings. Risk assessment modeling was accomplished using Waikato Environment for Knowledge Analysis (WEKA) open-sourced tool, which supported comparative assessment of the relative performance of the five ML algorithms when applied to risk prediction. To align with current conventions for clinical classification of disease severity, predicting PD risk was treated as a ‘classification problem’, where patients were sorted into two categories based on disease severity and ‘low risk PD’ was defined as no or mild gum disease (‘controls’) or ‘high risk PD’ defined as moderate to severe disease (‘cases’). To assess the predictive performance of models, the study compared performance of ML algorithms applying analysis of recall, specificity, area under the curve, precision, F-measure and Matthew’s correlation coefficient (MCC) and receiver operating characteristic (ROC) curve. A tenfold-cross validation was performed. External validation of the resultant models was achieved by creating validation data subsets applying random selection of approximately 10% of each class of data proportionately. Findings from this study have prognostic implications for assessing PD risk. Models evolved in the present study have translational value in that they can be incorporated into the Electronic Health Record (EHR) to support POC screening. Additionally, the study has defined relative performance of PD risk prediction models across various MOC environments. Moreover, these findings have established the power ML application can serve to create a decision support tool for dental providers in assessing PD status, severity and inform treatment decisions. Further, such risk scores could also inform medical providers regarding the need for patient referrals and management of comorbid conditions impacted by presence of oral disease such as PD. Finally, this study illustrates the benefit of the integrated medical and dental care delivery environment for detecting risk of periodontitis at a stage when implementation of proven interventions could delay and even prevent disease progression. Keywords: Periodontitis, Risk Assessment, Interprofessional Relations, Machine learning, Electronic Health Records, Decision Support System

Use of electronic medical records and biomarkers to manage risk and resource efficiencies

Peer reviewedPublisher PD

Integrating Behavioral Health & Primary Care in New Hampshire: A Path Forward to Sustainable Practice & Payment Transformation

New Hampshire residents face challenges with behavioral and physical health conditions and the interplay between them. National studies show the costs and the burden of illness from behavioral health conditions and co-occurring chronic health conditions that are not adequately treated in either primary care or behavioral health settings. Bringing primary health and behavioral health care together in integrated care settings can improve outcomes for both behavioral and physical health conditions. Primary care integrated behavioral health works in conjunction with specialty behavioral health providers, expanding capacity, improving access, and jointly managing the care of patients with higher levels of acuity In its work to improve the health of NH residents and create effective and cost-effective systems of care, the NH Citizens Health Initiative (Initiative) created the NH Behavioral Health Integration Learning Collaborative (BHI Learning Collaborative) in November of 2015, as a project of its Accountable Care Learning Network (NHACLN). Bringing together more than 60 organizations, including providers of all types and sizes, all of the state’s community mental health centers, all of the major private and public insurers, and government and other stakeholders, the BHI Learning Collaborative built on earlier work of a NHACLN Workgroup focused on improving care for depression and co-occurring chronic illness. The BHI Learning Collaborative design is based on the core NHACLN philosophy of “shared data and shared learning” and the importance of transparency and open conversation across all stakeholder groups. The first year of the BHI Learning Collaborative programming included shared learning on evidence-based practice for integrated behavioral health in primary care, shared data from the NH Comprehensive Healthcare Information System (NHCHIS), and work to develop sustainable payment models to replace inadequate Fee-for-Service (FFS) revenues. Provider members joined either a Project Implementation Track working on quality improvement projects to improve their levels of integration or a Listen and Learn Track for those just learning about Behavioral Health Integration (BHI). Providers in the Project Implementation Track completed a self-assessment of levels of BHI in their practice settings and committed to submit EHR-based clinical process and outcomes data to track performance on specified measures. All providers received access to unblinded NHACLN Primary Care and Behavioral Health attributed claims data from the NHCHIS for provider organizations in the NH BHI Learning Collaborative. Following up on prior work focused on developing a sustainable model for integrating care for depression and co-occurring chronic illness in primary care settings, the BHI Learning Collaborative engaged consulting experts and participants in understanding challenges in Health Information Technology and Exchange (HIT/HIE), privacy and confidentiality, and workforce adequacy. The BHI Learning Collaborative identified a sustainable payment model for integrated care of depression in primary care. In the process of vetting the payment model, the BHI Learning Collaborative also identified and explored challenges in payment for Substance Use Disorder Screening, Brief Intervention and Referral to Treatment (SBIRT). New Hampshire’s residents will benefit from a health care system where primary care and behavioral health are integrated to support the care of the whole person. New Hampshire’s current opiate epidemic accentuates the need for better screening for behavioral health issues, prevention, and treatment referral integrated into primary care. New Hampshire providers and payers are poised to move towards greater integration of behavioral health and primary care and the Initiative looks forward to continuing to support progress in supporting a path to sustainable integrated behavioral and primary care

Organizing the U.S. Health Care Delivery System for High Performance

Analyzes the fragmentation of the healthcare delivery system and makes policy recommendations -- including payment reform, regulatory changes, and infrastructure -- for creating mechanisms to coordinate care across providers and settings

Accountable Care Organizations in California: Promise and Performance

California has more accountable care organizations (ACOs) than any other state in the country, with particularly rapid growth over the past two years. This report introduces new evidence that ACOs improve the quality of care, increase patient satisfaction, and may reduce costs