Need for diagnostic-centric care in dentistry: A case study from the Marshfield Clinic Health System

ESTABLISHING A QUALITY IMPROVEMENT CULTURE WITHIN A LARGE INTEGRATED MEDICAL-DENTAL HEALTH SYSTEM WITH A POPULATION BASED FOCUS

OBJECTIVES

Quality improvement strategies have been an integral part of healthcare to attain improved care delivery and effective health outcomes. The dental quality initiative improvement (DQII) presented in this manuscript represents a case study of successful implementation of a quality improvement culture within a large integrated-medical-dental health system serving a largely rural population.

METHODS

The key elements of DQII included steering committee establishment, definition or dental quality measures and development/implementation of a dental quality analytics dashboard (DQAD) that provides relevant data on dental quality measures. Qualitative metrics were applied to look at the improvement in performance for the various measures relative to quality benchmarks.

RESULTS

DQII facilitated improved oversight of care continuity and provider performance surrounding quality measures at granular and/or institutional level. Improvement associated with care delivery performance relative to benchmarks was observed.

CONCLUSIONS

DQII further advanced the quality improvement culture prevalent in our learning healthcare environment with its focus on value-based care delivery. DQII initiative and establishment of DQAD provided ability to track performance in operational care delivery for dental providers in a clinical setting in real time.

Development of non-invasive diabetes risk prediction models as decision support tools designed for application in the dental clinical environment

The objective was to develop a predictive model using medical-dental data from an integrated electronic health record (iEHR) to identify individuals with undiagnosed diabetes mellitus (DM) in dental settings. Retrospective data retrieved from Marshfield Clinic Health System's data-warehouse was pre-processed prior to conducting analysis. A subset was extracted from the preprocessed dataset for external evaluation (Nvalidation) of derived predictive models. Further, subsets of 30%-70%, 40%-60% and 50%-50% case-to-control ratios were created for training/testing. Feature selection was performed on all datasets. Four machine learning (ML) classifiers were evaluated: logistic regression (LR), multilayer perceptron (MLP), support vector machines (SVM) and random forests (RF). Model performance was evaluated on Nvalidation. We retrieved a total of 5319 cases and 36,224 controls. From the initial 116 medical and dental features, 107 were used after performing feature selection. RF applied to the 50%-50% case-control ratio outperformed other predictive models over Nvalidation achieving a total accuracy (94.14%), sensitivity (0.941), specificity (0.943), F-measure (0.941), Mathews-correlation-coefficient (0.885) and area under the receiver operating curve (0.972). Future directions include incorporation of this predictive model into iEHR as a clinical decision support tool to screen and detect patients at risk for DM triggering follow-ups and referrals for integrated care delivery between dentists and physicians.

Evaluation of a Dental Diagnostic Terminology Subset

The objective of this study was to determine how well a subset of SNODENT, specifically designed for general dentistry, meets the needs of dental practitioners. Participants were asked to locate their written diagnosis for tooth conditions among the SNODENT terminology uploaded into an electronic dental record. Investigators found that 65% of providers’ original written diagnoses were in “agreement” with their selected SNODENT dental diagnostic subset concept(s).

Knowledgeability, attitude, and practice behaviors of primary care providers toward managing patients' oral health care in medical practice: Wisconsin statewide survey

BACKGROUND

In this study, the authors sought to explore the receptivity, preparedness, and rates of adoption of integrated medical-dental models of care (MOCs) in the practice setting among primary care providers (PCPs) treating patients with diabetes mellitus (DM).

METHODS

The authors conducted an anonymous statewide survey targeting PCPs across a range of Wisconsin-based practice settings to evaluate knowledgeability, attitude, practice behaviors, and perceived barriers to oral health screening in a medical setting. Qualitative analytical approaches included thematic analyses applied to evaluate the status of and barriers to integrated medical-dental MOC adoption.

RESULTS

The integrated medical-dental MOC adoption rate was 34%. Top perceived barriers to integrated medical-dental MOC adoption included insurance coverage (71%) and care access (70%). A total of 39% indicated competency for educating patients about the association between DM and periodontitis. Although 72% of PCPs indicated optimal periodicity for oral health assessment as frequent, 39% reported frequently conducting such assessments.

CONCLUSIONS

Although PCPs indicate receptivity to integrated medical-dental MOCs, PCPs identify suboptimal education, lack of adequate training in oral-systemic disease assessment, and barriers to oral health care access as barriers to integrated medical-dental MOC adoption.

PRACTICAL IMPLICATIONS

Integrated medical-dental MOC adoption in care delivery to patients with DM remains below average. Interdisciplinary efforts and education are needed to address identified barriers to care integration.