Translating periodontal data to knowledge in a learning health system

BACKGROUND : A learning health system (LHS) is a health system in which patients and clinicians work together to choose care on the basis of best evidence and to drive discovery as a natural outgrowth of every clinical encounter to ensure the right care at the right time. An LHS for dentistry is now feasible, as an increased number of oral health care encounters are captured in electronic health records (EHRs). METHODS : The authors used EHRs data to track periodontal health outcomes at 3 large dental institutions. The 2 outcomes of interest were a new periodontitis case (for patients who had not received a diagnosis of periodontitis previously) and tooth loss due to progression of periodontal disease. RESULTS : The authors assessed a total of 494,272 examinations (new periodontitis outcome: n = 168,442; new tooth loss outcome: n = 325,830), representing a total of 194,984 patients. Dynamic dashboards displaying performance on both measures over time allow users to compare demographic and risk factors for patients. The incidence of new periodontitis and tooth loss was 4.3% and 1.2%, respectively. CONCLUSIONS: Periodontal disease, diagnosis, prevention, and treatment are particularly well suited for an LHS model. The results showed the feasibility of automated extraction and interpretation of critical data elements from the EHRs. The 2 outcome measures are being implemented as part of a dental LHS. The authors are using this knowledge to target the main drivers of poorer periodontal outcomes in a specific patient population, and they continue to use clinical health data for the purpose of learning and improvement. PRACTICAL IMPLICATIONS : Dental institutions of any size can conduct contemporaneous self-evaluation and immediately implement targeted strategies to improve oral health outcomes.US Department of Health and Human Services, National Institutes of Health, and National Institute of Dental and Craniofacial Research.https://jada.ada.orgam2023Dental Management Science

Caries risk documentation and prevention : eMeasures for dental electronic health records

BACKGROUND: Longitudinal patient level dataavailable in the electronic health record (EHR)allows for the development, implementation, and validations of dental quality measures (eMeasures). Objective We report the feasibility and validity of implementing two eMeasures. The eMeasures determined the proportion of patients receiving a caries risk assessment (eCRA) and corresponding appropriate risk-based preventative treatments for patients at elevated risk of caries (appropriateness of care [eAoC]) in two academic institutions and one accountable care organization, in the 2019 reporting year. METHODS: Both eMeasures define the numerator and denominator beginning at the patient level, populations’ specifications, and validated the automated queries. For eCRA, patients who completed a comprehensive or periodic oral evaluation formed the denominator, and patients of any age who received a CRA formed the numerator. The eAoC evaluated the proportion of patients at elevated caries risk who received the corresponding appropriate risk-based preventative treatments. RESULTS: EHR automated queries identified in three sites 269,536 patients who met the inclusion criteria for receiving a CRA. The overall proportion of patients who received a CRA was 94.4% (eCRA). In eAoC, patients at elevated caries risk levels (moderate, high, or extreme) received fluoride preventive treatment ranging from 56 to 93.8%. For patients at high and extreme risk, antimicrobials were prescribed more frequently site 3 (80.6%) than sites 2 (16.7%) and 1 (2.9%). CONCLUSION: Patient-level data available in the EHRs can be used to implement process-ofcare dental eCRA and AoC, eAoC measures identify gaps in clinical practice. EHR-based measures can be useful in improving delivery of evidence-based preventative treatments to reduce risk, prevent tooth decay, and improve oral health.U.S. Department of Health and Human Services, National Institutes of Health, National Institute of Dental and Craniofacial Research.http://www.thieme.com/books-main/clinical-informatics/product/4433-aci-applied-clinical-informaticsDental Management Science

Development of quality measures to assess tooth decay outcomes from electronic health record data

Objectives: To develop outcomes of care quality measures derived from the dental electronic health record (EHR) to assess the occurrence and timely treatment of tooth decay. Methods: Quality measures were developed to assess whether decay was treated within 6 months and if new decay occurred in patients seen. Using EHR-derived data of the state of each tooth surface, algorithms compared the patient's teeth at different dates to determine if decay was treated or new decay had occurred. Manual chart reviews were conducted at three sites to validate the measures. The measures were implemented and scores were calculated for three sites over four calendar years, 2016 through 2019. Results: About 954 charts were manually reviewed for the timely treatment of tooth decay measure, with measure performance of sensitivity 97%, specificity 85%, positive predictive value (PPV) 91%, negative predictive value (NPV) 95%. About 739 charts were reviewed for new decay measure, with sensitivity 94%, specificity 99%, PPV 99%, and NPV 94%. Across all sites and years, 52.8% of patients with decay were fully treated within 6 months of diagnosis (n = 247,959). A total of 23.8% of patients experienced new decay, measured at an annual exam (n = 640,004). Conclusion: Methods were developed and validated for assessing timely treatment of decay and occurrence of new decay derived from EHR data, creating effective outcome measures. These EHR-based quality measures produce accurate and reliable results that support efforts and advancement in quality assessment, quality improvement, patient care and research

Assessing the validity of existing dental sealant quality measures.

BackgroundAlthough sealants are highly effective in preventing caries in children, placement rates continue to be low. The authors' goals were to implement and assess the performance of 2 existing sealant quality measures against a manual audit of charts at 4 dental institutions and to identify measurement gaps that may be filled by using data from electronic health records.MethodsThe authors evaluated the performance of 2 quality measures designed for claims-based data: the Dental Quality Alliance (DQA) sealant measure, which includes patients at risk of developing elevated caries, and the Oregon Health Authority (OHA) sealant measure (irrespective of caries risk). The authors adapted and validated these measures at 4 sites: 3 dental schools and 1 large dental accountable care organization.ResultsThe overall modified DQA and modified OHA measure scores in the 6- through 9-year-old age group were 37.0% and 31.6% and in the 10- through 14-year-old age group were 15.8% and 6.6%, respectively. Results from the manual review of charts showed that 67.6% of children who did not receive sealants did not have any teeth to seal because their molars had not yet erupted, had been extracted, had been sealed previously, or had existing caries or restorations.ConclusionsBoth the DQA and OHA measures, which rely mainly on Current Dental Terminology procedure codes, led to underestimation of the care delivered from a practice perspective. Future sealant quality measures should exclude patients whose teeth cannot be sealed.Practical implicationsThis study's results support the suitability of using electronic health record data for assessing the quality of oral health care, particularly for measuring sealant placement in children

Translating periodontal data to knowledge in a learning health system

BackgroundA learning health system (LHS) is a health system in which patients and clinicians work together to choose care on the basis of best evidence and to drive discovery as a natural outgrowth of every clinical encounter to ensure the right care at the right time. An LHS for dentistry is now feasible, as an increased number of oral health care encounters are captured in electronic health records (EHRs).MethodsThe authors used EHRs data to track periodontal health outcomes at 3 large dental institutions. The 2 outcomes of interest were a new periodontitis case (for patients who had not received a diagnosis of periodontitis previously) and tooth loss due to progression of periodontal disease.ResultsThe authors assessed a total of 494,272 examinations (new periodontitis outcome: n = 168,442; new tooth loss outcome: n = 325,830), representing a total of 194,984 patients. Dynamic dashboards displaying performance on both measures over time allow users to compare demographic and risk factors for patients. The incidence of new periodontitis and tooth loss was 4.3% and 1.2%, respectively.ConclusionsPeriodontal disease, diagnosis, prevention, and treatment are particularly well suited for an LHS model. The results showed the feasibility of automated extraction and interpretation of critical data elements from the EHRs. The 2 outcome measures are being implemented as part of a dental LHS. The authors are using this knowledge to target the main drivers of poorer periodontal outcomes in a specific patient population, and they continue to use clinical health data for the purpose of learning and improvement.Practical implicationsDental institutions of any size can conduct contemporaneous self-evaluation and immediately implement targeted strategies to improve oral health outcomes

Evaluating quality of dental care among patients with diabetes: Adaptation and testing of a dental quality measure in electronic health records.

BackgroundPatients with diabetes are at increased risk of developing oral complications, and annual dental examinations are an endorsed preventive strategy. The authors evaluated the feasibility and validity of implementing an automated electronic health record (EHR)-based dental quality measure to determine whether patients with diabetes received such evaluations.MethodsThe authors selected a Dental Quality Alliance measure developed for claims data and adapted the specifications for EHRs. Automated queries identified patients with diabetes across 4 dental institutions, and the authors manually reviewed a subsample of charts to evaluate query performance. After assessing the initial EHR measure, the authors defined and tested a revised EHR measure to capture better the oral care received by patients with diabetes.ResultsIn the initial and revised measures, the authors used EHR automated queries to identify 12,960 and 13,221 patients with diabetes, respectively, in the reporting year. Variations in the measure scores across sites were greater with the initial measure (range, 36.4-71.3%) than with the revised measure (range, 78.8-88.1%). The automated query performed well (93% or higher) for sensitivity, specificity, and positive and negative predictive values for both measures.ConclusionsThe results suggest that an automated EHR-based query can be used successfully to measure the quality of oral health care delivered to patients with diabetes. The authors also found that using the rich data available in EHRs may help estimate the quality of care better than can relying on claims data.Practical implicationsDetailed clinical patient-level data in dental EHRs may be useful to dentists in evaluating the quality of dental care provided to patients with diabetes

Measuring sealant placement in children at the dental practice level.

BACKGROUND: Although sealants are an established and recommended caries-preventive treatment, many children still fail to receive them. In addition, research has shown that existing measures underestimate care by overlooking the sealable potential of teeth before evaluating care. To address this, the authors designed and evaluated 3 novel dental electronic health record-based clinical quality measures that evaluate sealant care only after assessing the sealable potential of teeth. METHODS: Measure I recorded the proportion of patients with sealable teeth who received sealants. Measure II recorded the proportion of patients who had at least 1 of their sealable teeth sealed. Measure III recorded the proportion of patients who received sealant on all of their sealable teeth. RESULTS: On average, 48.1% of 6- through 9-year-old children received 1 or more sealants compared with 32.4% of 10- through 14-year-olds (measure I). The average measure score decreased for patients who received sealants for at least 1 of their sealable teeth (measure II) (43.2% for 6- through 9-year-olds and 28.4% for 10- through 14-year-olds). Fewer children received sealants on all eligible teeth (measure III) (35.5% of 6- through 9-year-olds and 21% of 10- through 14-year-olds received sealant on all eligible teeth). Among the 48.5% who were at elevated caries risk, the sealant rates were higher across all 3 measures. CONCLUSIONS: A valid and actionable practice-based sealant electronic measure that evaluates sealant treatment among the eligible population, both at the patient level and the tooth level, has been developed. PRACTICAL IMPLICATIONS: The measure developed in this work provides practices with patient-centered and actionable sealant quality measures that aim to improve oral health outcomes

Tobacco screening and cessation efforts by dental providers: A quality measure evaluation

OBJECTIVES:The purpose of this study was to adapt, test, and evaluate the implementation of a primary care "Preventive care and Screening" meaningful use quality measure for tobacco use, in dental institutions. We determined the percentage of dental patients screened for tobacco use, and the percentage of tobacco users who received cessation counseling. METHODS:We implemented the dental quality measure (DQM), in three dental schools and a large dental accountable care organization. An automated electronic health record (EHR) query identified patients 18 years and older who were screened for tobacco use one or more times within 24 months, and who received cessation counseling intervention if identified as a tobacco user. We evaluated EHR query performance with a manual review of a subsample of charts. RESULTS:Across all four sites, in the reporting calendar year of 2015, a total of 143,675 patients met the inclusion criteria for the study. Within 24 months, including 2014 and 2015 calendar years, percentages of tobacco screening ranged from 79.7 to 99.9 percent, while cessation intervention percentages varied from 1 to 81 percent among sites. By employing DQM research methodology, we identified intervention gaps in clinical practice. CONCLUSIONS:We demonstrated the successful implementation of a DQM to evaluate screening rates for tobacco use and cessation intervention. There is substantial variation in the cessation intervention rates across sites, and these results are a call for action for the dental profession to employ tobacco evidence-based cessation strategies to improve oral health and general health outcomes

Developing and Testing Electronic Health Record-Derived Caries Indices.

Caries indices, the basis of epidemiologic caries measures, are not easily obtained in clinical settings. This study's objective was to design, test, and validate an automated program (Valid Electronic Health Record Dental Caries Indices Calculator Tool [VERDICT]) to calculate caries indices from an electronic health record (EHR). Synthetic use case scenarios and actual patient cases of primary, mixed, and permanent dentition, including decayed, missing, and filled teeth (DMFT/dmft) and tooth surfaces (DMFS/dmfs) were entered into the EHR. VERDICT measures were compared to a previously validated clinical electronic data capture (EDC) system and statistical program to calculate caries indices. Four university clinician-researchers abstracted EHR caries exam data for 45 synthetic use cases into the EDC and post-processed with SAS software creating a gold standard to compare the -VERDICT-derived caries indices. Then, 2 senior researchers abstracted EHR caries exam data and calculated caries indices for 24 patients, allowing further comparisons to VERDICT indices. Agreement statistics were computed among abstractors, and discrepancies were resolved by consensus. Agreement statistics between the 2 final-phase abstractors and the VERDICT measures showed extremely high concordance: Lin's concordance coefficients (LCCs) >0.99 for dmfs, dmft, DS, ds, DT, dt, ms, mt, FS, fs, FT, and ft; LCCs >0.95 for DMFS and DMFT; and LCCs of 0.92-0.93 for MS and MT. Caries indices, essential to developing primary health outcome measures for research, can be reliably derived from an EHR using VERDICT. Using these indices will enable population oral health management approaches and inform quality improvement efforts

Identifying Contributing Factors Associated With Dental Adverse Events Through a Pragmatic Electronic Health Record-Based Root Cause Analysis

Objective This study assessed contributing factors associated with dental adverse events (AEs). Methods Seven electronic health record-based triggers were deployed identifying potential AEs at 2 dental institutions. From 4106 flagged charts, 2 reviewers examined 439 charts selected randomly to identify and classify AEs using our dental AE type and severity classification systems. Based on information captured in the electronic health record, we analyzed harmful AEs to assess potential contributing factors; harmful AEs were defined as those that resulted in temporary moderate to severe harm, required hospitalization, or resulted in permanent moderate to severe harm. We classified potential contributing factors according to (1) who was involved (person), (2) what were they doing (tasks), (3) what tools/technologies were they using (tools/technologies), (4) where did the event take place (environment), (5) what organizational conditions contributed to the event? (organization), (6) patient (including parents), and (7) professional-professional collaboration. A blinded panel of dental experts conducted a second review to confirm the presence of an AE. Results Fifty-nine cases had 1 or more harmful AEs. Pain occurred most frequently (27.1%), followed by nerve injury (16.9%), hard tissue injury (15.2%), and soft tissue injury (15.2%). Forty percent of the cases were classified as "temporary not moderate to severe harm."Person (training, supervision, and fatigue) was the most common contributing factor (31.5%), followed by patient (noncompliance, unsafe practices at home, low health literacy, 17.1%), and professional-professional collaboration (15.3%). Conclusions Pain was the most common harmful AE identified. Person, patient, and professional-professional collaboration were the most frequently assessed factors associated with harmful AEs.</p