Achieving Large-Scale Quality Improvement in Primary Care Annual Wellness Visits and Hierarchical Condition Coding.

BACKGROUND: Completion of Medicare Annual Wellness Visits (AWV) and documentation of Hierarchical Condition Categories (HCC) are important metrics in accountable care organizations (ACO) with quality and financial implications. To improve performance in large healthcare organizations, quality improvement (QI) efforts need to be scaled up in a way that is feasible within available system-wide resources. OBJECTIVE: We describe a 3-year effort using a multifaceted QI framework called the fractal management system for AWV and HCC performance. DESIGN: Pre-post evaluation of a multi-level, health system-wide QI management system intervention between 2018 and 2020. The system provided project management, coaching, communications, feedback of performance, and health informatics. PARTICIPANTS: The intervention was delivered to all 97 primary care practices within an Ohio-based accountable care organization, comprising 72,603 attributed Medicare and Medicare Advantage patients as of 2018. Eighty-nine of these practices were included in the analysis. APPROACH: AWV completion was defined as percent of eligible patients with a documented AWV during the calendar year. HCC completion was defined as documented reassessment of all prior-year HCC conditions. KEY RESULTS: AWV completion at the practice level increased from 23.7% (SD .14) in 2018 to 34.9% (SD .18) in 2019, and 59.8% (SD .17) in 2020. This was a statistically significant effect of time on AWV completion rates overall (F[2, 87] = 164.43, p < .000). More than half (56.2%) of practices met or exceeded the 60% goal in 2020. Practice-level HCC completion tracking started in 2019 (M = 75.9%, SD 7.4%) and increased in 2020 (M = 79.7%, SD 7.1%); t(172) = 2.0, p < .001. CONCLUSIONS: AWV and HCC performance goals were met in 2020, despite service disruptions due to COVID-19. The QI approach we used is applicable to other problems and other large healthcare systems.

Redesigning Kidney Disease Care to Improve Value Delivery.

This article describes the articulation, development, and deployment of a machine learning (ML) model-driven value solution for chronic kidney disease (CKD) in a health system. The ML model activated an electronic medical record (EMR) trigger that alerted CKD patients to seek primary care. Simultaneously, primary care physicians (PCPs) received an alert that a CKD patient needed an appointment. Using structured checklists, PCPs addressed and controlled comorbid conditions, reconciled drug dosing and choice to CKD stage, and ordered prespecified laboratory and imaging tests pertinent to CKD. After completion of checklist prescribed tasks, PCPs referred patients to nephrology. CKD patients had multiple comorbidities and ML recognition of CKD provided a facile insight into comorbid burden. Operational results of this program have exceeded expectations and the program is being expanded to the entire health system. This paradigm of ML-driven, checklist-enabled care can be used agnostic of EMR platform to deliver value in CKD through structured engagement of complexity in health systems.

Stratifying for Value: An Updated Population Health Risk Stratification Approach.

Most risk stratification approaches attempt to predict clinical outcomes rather than value. For a provider organization or health system to have financial success in value-based contracting, future risk models must analyze costs as well as disease burden. The purpose of this study was to create a customized risk stratification algorithm that considered a patient's medical spend alongside disease burden while delivering a scoring system that improves the efficiency of a care coordination program. The authors focused on University Hospitals (UH) Health System's Accountable Care Organization population of 554,805 because this patient cohort is engaged with UH's primary care network and has the most robust data. The 5-category risk algorithm was found to be meaningful and impactful after integrating the foundation of the Minnesota Tiering system with an expanded comorbidity list and weighting the result by the previous 12 months of medical spend. This new technique can identify patients in need of intensive care coordination. The complex risk tier of the stratification system reduces the number of patients from 551,045 to 27,552, or 5% of the patient population, and accounts for 67.9% ($1,107,822,887) of total annual medical spend. Expanding care coordination efforts to patients in the top 2 tiers would account for 15% of the patients and 83.2% ($1,357,545,872) of annual medical spend. The novelty of the new approach allows clinical teams to focus intense resources on a smaller sample of the patient population and to identify chronic conditions contributing to costs, and feel confident that they have greater explanatory power regarding value.

Are physicians and patients in agreement? Exploring dyadic concordance.

Dyadic concordance in physician-patient interactions can be defined as the extent of agreement between physicians and patients in their perceptions of the clinical encounter. The current research specifically examined two types of concordance: informational concordance-the extent of agreement in physician and patient responses regarding patient information (education, self-rated health, pain); and interactional concordance-the extent of physician-patient agreement regarding the patient's level of confidence and trust in the physician and the perceived quality of explanations concerning diagnosis and treatment. Using a convenience sample of physicians and patients (N = 50 dyads), a paired survey method was tested, which measured and compared physician and patient reports to identify informational and interactional concordances. Factors potentially related to dyadic concordance were also measured, including demographic characteristics (patient race, gender, age, and education) and clinical factors (whether this was a first visit and physician specialty in family medicine or oncology). The paired survey showed informational discordances, as physicians tended to underestimate patients' pain and overestimate patient education. Interactional discordances included overestimating patients' understanding of diagnosis and treatment explanations and patients' level of confidence and trust. Discordances were linked to patient dissatisfaction with physician listening, having unanswered questions, and feeling the physician had not spent enough time. The paired survey method effectively identified physician-patient discordances that may interfere with effective medical practice; this method may be used in various settings to identify potential areas of improvement in health communication and education.

Revisiting patient communication training: an updated needs assessment and the AGENDA model.

OBJECTIVE: To assess physician needs for patient-centered communication training for medical consultations and to develop an updated patient training curriculum. METHODS: An online needs assessment was distributed through physician email listserves at the University of Florida College of Medicine. Frequency tabulation and content analyses were conducted to assess patient communication themes. RESULTS: Responses were received from 336 physicians. Physicians reported that patients are reluctant to ask questions when they do not understand information related to their medical condition, treatment plan, or medical advice. Furthermore, physicians reported that a lack of patient negotiation inhibits patient-centered communication and may negatively influence patient adherence and compliance. Based on these results the AGENDA model was created for patient training and consists of (1) agenda setting; (2) goals for health; (3) expressing concerns, questions, and negotiations; (4) navigating health literacy issues; (5) disclosing detailed information; and (6) active types of listening. CONCLUSION: This study supports revisiting patient communication training and tailoring future training interventions to specific communities. PRACTICE IMPLICATIONS: The AGENDA model can be used to train patients to enhance patient-centered communication with physicians. Additionally, patient communication training could help to address the barriers to care identified by the physicians in our study.

Hepatocyte paraffin 1 antigen as a biomarker for early diagnosis of Barrett esophagus.

We evaluated hepatocyte paraffin 1 (HepPar1) antigen expression, a sensitive marker of small intestinal differentiation, in combination with morphologic features to demonstrate intestinal differentiation in cases equivocal for Barrett esophagus (BE). Clinicopathologic features and HepPar1 expression were recorded for 54 BE cases, 45 consistent with reflux esophagitis (RE) cases, and 65 "suspicious" for BE (SBE) cases. The SBE category included RE cases with 2 or more morphologic changes associated with BE or metaplastic reaction to injury (eg, multilayered epithelium, squamous islands, goblet cell mimickers, pancreatic metaplasia). HepPar1 was expressed in all 54 BE cases, 4 of 45 RE cases, and 24 of 65 SBE cases. In SBE cases, 2 or more morphologic changes were associated with HepPar1 expression in 37% of cases (24/65), 3 or more features in 59% (13/22), and 4 or more features in 100% (4/4) (P ≤ .004). The combination of certain morphologic changes and HepPar1 expression in clinically suspicious distal esophageal biopsy cases without goblet cells supports the presence of evolving intestinal metaplasia.