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1.
Learn Health Syst ; 8(2): e10398, 2024 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-38633022

RESUMO

The overarching goal of the third scientific oral health symposium was to introduce the concept of a learning health system to the dental community and to identify and discuss cutting-edge research and strategies using data for improving the quality of dental care and patient safety. Conference participants included clinically active dentists, dental researchers, quality improvement experts, informaticians, insurers, EHR vendors/developers, and members of dental professional organizations and dental service organizations. This report summarizes the main outputs of the third annual OpenWide conference held in Houston, Texas, on October 12, 2022, as an affiliated meeting of the American Dental Association (ADA) 2022 annual conference.

2.
J Am Dent Assoc ; 2024 Apr 06.
Artigo em Inglês | MEDLINE | ID: mdl-38583172

RESUMO

BACKGROUND: Dental sealants are effective for the prevention of caries in children at elevated risk levels, and increasing the proportion of children and adolescents who have dental sealants on 1 or more molars is a Healthy People 2030 objective. Electronic health record (EHR)-based clinical decision support systems (CDSSs) have the ability to improve patient care. A dental quality measure related to dental sealant placement for children at elevated risk of caries was targeted for improvement using a CDSS. METHODS: A validated dental quality measure was adapted to assess a patient's need for dental sealant placement. A CDSS was implemented to advise care team members whether a child was at elevated risk of developing caries and had sealant-eligible first or second molars. Data on dental sealant placement at examination visits during a 5-year period were analyzed, including 32 months before CDSS implementation and 28 months after CDSS implementation. RESULTS: From January 1, 2018, through December 31, 2022, the authors assessed 59,047 examination visits for children at elevated risk of developing caries and with sealant-eligible teeth. With the implementation of a CDSS and training to support the clinical care team members in September 2020, the appropriate placement of dental sealants at examination visits increased from 27% through 60% (P < .00001). CONCLUSIONS: Integration of a CDSS into the EHR as part of a quality improvement program was effective in increasing the delivery of sealants in eligible first and second molars of children aged 5 through 15 years and considered at high risk of developing caries. PRACTICAL IMPLICATIONS: An EHR-based CDSS can be implemented to improve standardization and provide timely and appropriate patient care in dental practices.

3.
J Dent ; 144: 104921, 2024 Mar 02.
Artigo em Inglês | MEDLINE | ID: mdl-38437976

RESUMO

OBJECTIVES: This study aimed to identify predictors associated with the tooth loss phenotype in a large periodontitis patient cohort in the university setting. METHODS: Information on periodontitis patients and nineteen factors identified at the initial visit was extracted from electronic health records. The primary outcome is tooth loss phenotype (presence or absence of tooth loss). Prediction models were built on significant factors (single or combinatory) selected by the RuleFit algorithm, and these factors were further adopted by regression models. Model performance was evaluated by Area Under the Receiver Operating Characteristic Curve (AUROC) and Area Under the Precision-Recall Curve (AUPRC). Associations between predictors and the tooth loss phenotype were also evaluated by classical statistical approaches to validate the performance of machine learning models. RESULTS: In total, 7840 patients were included. The machine learning model predicting the tooth loss phenotype achieved AUROC of 0.71 and AUPRC of 0.66. Age, periodontal diagnosis, number of missing teeth at baseline, furcation involvement, and tooth mobility were associated with the tooth loss phenotype in both machine learning and classical statistical models. CONCLUSIONS: The rule-based machine learning approach improves model explainability compared to classical statistical methods. However, the model's generalizability needs to be further validated by external datasets. CLINICAL SIGNIFICANCE: Predictors identified by the current machine learning approach using the RuleFit algorithm had clinically relevant thresholds in predicting the tooth loss phenotype in a large and diverse periodontitis patient cohort. The results of this study will assist clinicians in performing risk assessment for periodontitis at the initial visit.

4.
JAMIA Open ; 7(1): ooae018, 2024 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-38476372

RESUMO

Objectives: The use of interactive mobile health (mHealth) applications to monitor patient-reported postoperative pain outcomes is an emerging area in dentistry that requires further exploration. This study aimed to evaluate and improve the usability of an existing mHealth application. Materials and methods: The usability of the application was assessed iteratively using a 3-phase approach, including a rapid cognitive walkthrough (Phase I), lab-based usability testing (Phase II), and in situ pilot testing (Phase III). The study team conducted Phase I, while providers and patients participated in Phase II and III. Results: The rapid cognitive walkthrough identified 23 potential issues that could negatively impact user experience, with the majority classified as system issues. The lab-based usability testing yielded 141 usability issues.; 43% encountered by patients and 57% by dentists. Usability problems encountered during pilot testing included undelivered messages due to mobile phone carrier and service-related issues, errors in patients' phone number data entry, and problems in provider training. Discussion: Through collaborative and iterative work with the vendor, usability issues were addressed before launching a trial to assess its efficacy. Conclusion: The usability of the mHealth application for postoperative dental pain was remarkably improved by the iterative analysis and interdisciplinary collaboration.

5.
BMC Oral Health ; 24(1): 201, 2024 Feb 07.
Artigo em Inglês | MEDLINE | ID: mdl-38326805

RESUMO

BACKGROUND: Dental Patient Reported Outcomes (PROs) relate to a dental patient's subjective experience of their oral health. How practitioners and patients value PROs influences their successful use in practice. METHODS: Semi-structured interviews were conducted with 22 practitioners and 32 patients who provided feedback on using a mobile health (mHealth) platform to collect the pain experience after dental procedures. A themes analysis was conducted to identify implementation barriers and facilitators. RESULTS: Five themes were uncovered: (1) Sense of Better Care. (2) Tailored Follow-up based on the dental procedure and patient's pain experience. (3) Effective Messaging and Alerts. (4) Usable Digital Platform. (5) Routine mHealth Integration. CONCLUSION: Frequent automated and preferably tailored follow-up messages using an mHealth platform provided a positive care experience for patients, while providers felt it saved them time and effort. Patients thought that the mHealth questionnaires were well-developed and of appropriate length. The mHealth platform itself was perceived as user-friendly by users, and most would like to continue using it. PRACTICAL IMPLICATIONS: Patients are prepared to use mobile phones to report their pain experience after dental procedures. Practitioners will be able to close the post-operative communication gap with their patients, with little interruption of their workflow.


Assuntos
Telefone Celular , Humanos , Dor , Odontólogos , Medidas de Resultados Relatados pelo Paciente , Odontologia
6.
J Patient Saf ; 20(3): 177-185, 2024 Apr 01.
Artigo em Inglês | MEDLINE | ID: mdl-38345377

RESUMO

OBJECTIVE: Despite the many advancements made in patient safety over the past decade, combating diagnostic errors (DEs) remains a crucial, yet understudied initiative toward improvement. This study sought to understand the perception of dental patients who have experienced a dental diagnostic failure (DDF) and to identify patient-centered strategies to help reduce future occurrences of DDF. METHODS: Through social media recruitment, we conducted a screening survey, initial assessment, and 67 individual patient interviews to capture the effects of misdiagnosis, missed diagnosis, or delayed diagnosis on patient lives. Audio recordings of patient interviews were transcribed, and a hybrid thematic analysis approach was used to capture details about 4 main domains of interest: the patient's DDF experience, contributing factors, impact, and strategies to mitigate future occurrences. RESULTS: Dental patients endured prolonged suffering, disease progression, unnecessary treatments, and the development of new symptoms as a result of experiencing DE. Poor provider communication, inadequate time with provider, and lack of patient self-advocacy and health literacy were among the top attributes patients believed contributed to the development of a DE. Patients suggested that improvements in provider chairside manners, more detailed patient diagnostic workups, and improving personal self-advocacy; along with enhanced reporting systems, could help mitigate future DE. CONCLUSIONS: This study demonstrates the valuable insight the patient perspective provides in understanding DEs, therefore aiding the development of strategies to help reduce the occurrences of future DDF events. Given the challenges patients expressed, there is a significant need to create an accessible reporting system that fosters constructive clinician learning.


Assuntos
Letramento em Saúde , Mídias Sociais , Humanos , Pacientes
7.
J Clin Periodontol ; 51(5): 547-557, 2024 May.
Artigo em Inglês | MEDLINE | ID: mdl-38212876

RESUMO

AIM: To develop and validate an automated electronic health record (EHR)-based algorithm to suggest a periodontal diagnosis based on the 2017 World Workshop on the Classification of Periodontal Diseases and Conditions. MATERIALS AND METHODS: Using material published from the 2017 World Workshop, a tool was iteratively developed to suggest a periodontal diagnosis based on clinical data within the EHR. Pertinent clinical data included clinical attachment level (CAL), gingival margin to cemento-enamel junction distance, probing depth, furcation involvement (if present) and mobility. Chart reviews were conducted to confirm the algorithm's ability to accurately extract clinical data from the EHR, and then to test its ability to suggest an accurate diagnosis. Subsequently, refinements were made to address limitations of the data and specific clinical situations. Each refinement was evaluated through chart reviews by expert periodontists at the study sites. RESULTS: Three-hundred and twenty-three charts were manually reviewed, and a periodontal diagnosis (healthy, gingivitis or periodontitis including stage and grade) was made by expert periodontists for each case. After developing the initial version of the algorithm using the unmodified 2017 World Workshop criteria, accuracy was 71.8% for stage alone and 64.7% for stage and grade. Subsequently, 16 modifications to the algorithm were proposed and 14 were accepted. This refined version of the algorithm had 79.6% accuracy for stage alone and 68.8% for stage and grade together. CONCLUSIONS: Our findings suggest that a rule-based algorithm for suggesting a periodontal diagnosis using EHR data can be implemented with moderate accuracy in support of chairside clinical diagnostic decision making, especially for inexperienced clinicians. Grey-zone cases still exist, where clinical judgement will be required. Future applications of similar algorithms with improved performance will depend upon the quality (completeness/accuracy) of EHR data.


Assuntos
Gengivite , Doenças Periodontais , Periodontite , Humanos , Registros Eletrônicos de Saúde , Doenças Periodontais/diagnóstico , Algoritmos
8.
J Am Dent Assoc ; 155(1): 6, 2024 01.
Artigo em Inglês | MEDLINE | ID: mdl-38032591
9.
JMIR Mhealth Uhealth ; 11: e49677, 2023 10 20.
Artigo em Inglês | MEDLINE | ID: mdl-37933185

RESUMO

Background: Postoperative dental pain is pervasive and can affect a patient's quality of life. Adopting a patient-centric approach to pain management involves having contemporaneous information about the patient's experience of pain and using it to personalize care. Objective: In this study, we evaluated the use of a mobile health (mHealth) platform to collect pain-related patient-reported outcomes over 7 days after the patients underwent pain-inducing dental procedures; we then relayed the information to the dentist and determined its impact on the patient's pain experience. Methods: The study used a cluster-randomized experimental study design with an intervention arm where patients were prompted to complete a series of questions relating to their pain experience after receiving automated text notifications on their smartphone on days 1, 3, 5, and 7, with the resulting information fed back to dentists, and a control arm where patients received usual care. Providers were randomized, and patients subsequently assumed the enrollment status of their providers. Providers or their staff identified eligible patients and invited them to participate in the study. Provider interviews and surveys were conducted to evaluate acceptance of the mHealth platform. Results: A total of 42 providers and 1525 patients participated. For the primary outcome (pain intensity on a 1 to 10 scale, with 10 being the most painful), intervention group patients reported an average pain intensity of 4.8 (SD 2.6), while those in the control group reported an average pain intensity of 4.7 (SD 2.8). These differences were not significant. There were also no significant differences in secondary outcomes, including pain interference with activity or sleep, patient satisfaction with pain management, or opioid prescribing. Patient surveys revealed reluctance to use the app was mostly due to technological challenges, data privacy concerns, and a preference for phone calls over texting. Providers had high satisfaction with the app and suggested integrating additional features, such as an in-system camera for patients to upload pictures and videos of the procedural site, and integration with the electronic health record system. Conclusions: While the mHealth platform did not have a significant impact on acute postoperative pain experience, patients and providers indicated improvement in patient-provider communication, patient-provider relationship, postoperative complication management, and ability to manage pain medication prescribing. Expanded collaboration between mHealth developers and frontline health care providers can facilitate the applicability of these platforms, further help improve its integration with the normal clinic workflow, and assist in moving toward a more patient-centric approach to pain management.


Assuntos
Qualidade de Vida , Telemedicina , Humanos , Analgésicos Opioides , Padrões de Prática Médica , Dor Pós-Operatória , Telemedicina/métodos
10.
J Am Dent Assoc ; 154(11): 975-983.e1, 2023 11.
Artigo em Inglês | MEDLINE | ID: mdl-37676186

RESUMO

BACKGROUND: Children are the patient subgroup with the lowest error tolerance regarding deep sedation (DS)-supported care. This study assessed the safety of DS-supported pediatric dental treatment carried out in an outpatient setting through retrospective review of patient charts. METHODS: An automated script was developed to identify charts of pediatric patients who underwent DS-supported dental procedures from 2017 through 2019 at a dental clinic. Charts were assessed for the presence of sedation-related adverse events (AEs). A panel of experts performed a second review and confirmed or refuted the designation of AE (by the first reviewer). AEs were classified with the Tracking and Reporting Outcomes of Procedural Sedation system. RESULTS: Of the 175 DS cases, 19 AEs were identified in 15 cases (8.60%). Using the Tracking and Reporting Outcomes of Procedural Sedation classification system, 7 (36.84%) events were related to the airway and breathing category, 9 (47.37%) were related to sedation quality (including a dizzy patient who fell at the checkout desk and sustained a head laceration), and 3 (15.79%) were classified as an allergy. CONCLUSION: This study suggests an AE (whether relatively minor or of potentially major consequence) occurs in 1 of every 12 DS cases involving pediatric patients, performed at an outpatient dental clinic. Larger studies are needed, in addition to root cause analyses. PRACTICAL IMPLICATIONS: As dentists increasingly pivot in the use of DS services from in-hospital to outpatient settings, patients expect comparable levels of safety. This work helps generate evidence to drive targeted efforts to improve the safety and reliability of pediatric outpatient sedation.


Assuntos
Sedação Profunda , Pacientes Ambulatoriais , Criança , Humanos , Sedação Profunda/efeitos adversos , Sedação Profunda/métodos , Reprodutibilidade dos Testes , Estudos Retrospectivos , Sedação Consciente/efeitos adversos , Atenção à Saúde
11.
Int J Med Inform ; 176: 105092, 2023 08.
Artigo em Inglês | MEDLINE | ID: mdl-37267811

RESUMO

BACKGROUND AND OBJECTIVE: Prescription drug abuse is a major factor leading to drug overdose deaths in the US and dentists are one of the leading prescribers of opioid pain medication. Knowing that Audit & Feedback (A&F) dashboards are an effective tool and are used as quality improvement interventions, we aimed to develop such dashboards personalized for dental providers which could allow them to monitor their own opioid prescribing performance. METHODS: In this paper we report on the process for designing the A&F dashboards for dentists which were developed by using an iterative human-centered design process. The results obtained from each iteration were used to enrich the information needs analyses, provide function testing, and guide the design decisions of the next iteration. RESULTS: Engaging dentists in the development and refinement of the dashboards while using the think-aloud protocol for user-testing, provided rapid feedback and identified areas that were confusing and needed either a redesign or additional explanatory content. The final version of dashboards consisted of displaying necessary information through easy to interpret visualizations and interactive features. These included providing access to current national and organizational prescribing guidelines, displaying changes in individual prescribing behavior over time, comparing individual prescribing rate to peer group rate and target rate, displaying procedure specific prescribing, integrating patient reported post-operative dental pain experience and providing navigation and interpretation tips for users. The dashboards were easy to learn and understand for the dentists and were deemed as worth using often in dental practice. CONCLUSION: Our research was able to demonstrate the creation of useful and usable A&F dashboards using data from electronic dental records and patient surveys, for dentists to effectively monitor their opioid prescribing behavior. Efficacy of the dashboards will be tested in future work.


Assuntos
Analgésicos Opioides , Padrões de Prática Médica , Humanos , Analgésicos Opioides/uso terapêutico , Retroalimentação , Odontólogos , Dor
12.
AMIA Jt Summits Transl Sci Proc ; 2023: 300-309, 2023.
Artigo em Inglês | MEDLINE | ID: mdl-37350885

RESUMO

Learning about diagnostic features and related clinical information from dental radiographs is important for dental research. However, the lack of expert-annotated data and convenient search tools poses challenges. Our primary objective is to design a search tool that uses a user's query for oral-related research. The proposed framework, Contrastive LAnguage Image REtrieval Search for dental research, Dental CLAIRES, utilizes periapical radiographs and associated clinical details such as periodontal diagnosis, demographic information to retrieve the best-matched images based on the text query. We applied a contrastive representation learning method to find images described by the user's text by maximizing the similarity score of positive pairs (true pairs) and minimizing the score of negative pairs (random pairs). Our model achieved a hit@3 ratio of 96% and a Mean Reciprocal Rank (MRR) of 0.82. We also designed a graphical user interface that allows researchers to verify the model's performance with interactions.

13.
J Patient Saf ; 19(5): 305-312, 2023 08 01.
Artigo em Inglês | MEDLINE | ID: mdl-37015101

RESUMO

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.


Assuntos
Registros Eletrônicos de Saúde , Erros Médicos , Humanos , Análise de Causa Fundamental
14.
J Periodontol ; 94(10): 1231-1242, 2023 10.
Artigo em Inglês | MEDLINE | ID: mdl-37063053

RESUMO

BACKGROUND: This study aimed to identify predictors associated with tooth loss in a large periodontitis patient cohort in the university setting using the machine learning approach. METHODS: Information on periodontitis patients and 18 factors identified at the initial visit was extracted from electronic health records. A two-step machine learning pipeline was proposed to develop the tooth loss prediction model. The primary outcome is tooth loss count. The prediction model was built on significant factors (single or combination) selected by the RuleFit algorithm, and these factors were further adopted by the count regression model. Model performance was evaluated by root-mean-squared error (RMSE). Associations between predictors and tooth loss were also assessed by a classical statistical approach to validate the performance of the machine learning model. RESULTS: In total, 7840 patients were included. The machine learning model predicting tooth loss count achieved RMSE of 2.71. Age, smoking, frequency of brushing, frequency of flossing, periodontal diagnosis, bleeding on probing percentage, number of missing teeth at baseline, and tooth mobility were associated with tooth loss in both machine learning and classical statistical models. CONCLUSION: The two-step machine learning pipeline is feasible to predict tooth loss in periodontitis patients. Compared to classical statistical methods, this rule-based machine learning approach improves model explainability. However, the model's generalizability needs to be further validated by external datasets.


Assuntos
Periodontite , Perda de Dente , Humanos , Estudos Retrospectivos , Universidades , Periodontite/complicações , Periodontite/diagnóstico , Aprendizado de Máquina
15.
Orthod Craniofac Res ; 26 Suppl 1: 98-101, 2023 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-36919982

RESUMO

Despite technological advances, challenges exist in US dental care, including variations in quality of care, access and untreated dental needs. The implementation of learning health systems (LHSs) in dentistry can help to address these challenges. LHSs use robust informatics infrastructure including data and technology to continuously measure and improve the quality and safety of care and can help to reduce costs and improve patient outcomes. The use of EHRs and standardized diagnostic terminologies are highlighted, as they allow for the storage and sharing of patient data, providing a comprehensive view of a patient's medical and dental history, and can be used to identify patterns and trends to improve the delivery of care. The BigMouth Dental Data Repository is an example of an informatic platform that aggregates patient data from multiple institutions and is being used to for scientific inquiry to improve oral health.


Assuntos
Informática , Saúde Bucal , Humanos , Assistência Odontológica
16.
J Public Health Dent ; 83(1): 33-42, 2023 03.
Artigo em Inglês | MEDLINE | ID: mdl-36224111

RESUMO

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.


Assuntos
Cárie Dentária , Registros Eletrônicos de Saúde , Humanos , Indicadores de Qualidade em Assistência à Saúde , Qualidade da Assistência à Saúde , Cárie Dentária/terapia
17.
AMIA Annu Symp Proc ; 2023: 904-912, 2023.
Artigo em Inglês | MEDLINE | ID: mdl-38222409

RESUMO

This study explored the usability of prompt generation on named entity recognition (NER) tasks and the performance in different settings of the prompt. The prompt generation by GPT-J models was utilized to directly test the gold standard as well as to generate the seed and further fed to the RoBERTa model with the spaCy package. In the direct test, a lower ratio of negative examples with higher numbers of examples in prompt achieved the best results with a F1 score of 0.72. The performance revealed consistency, 0.92-0.97 in the F1 score, in all settings after training with the RoBERTa model. The study highlighted the importance of seed quality rather than quantity in feeding NER models. This research reports on an efficient and accurate way to mine clinical notes for periodontal diagnoses, allowing researchers to easily and quickly build a NER model with the prompt generation approach.


Assuntos
Registros Odontológicos , Processamento de Linguagem Natural , Humanos
18.
BMC Oral Health ; 22(1): 581, 2022 12 09.
Artigo em Inglês | MEDLINE | ID: mdl-36494795

RESUMO

BACKGROUND: Patient-reported outcome measures provide an essential perspective on the quality of health care provided. However, how data are collected, how providers value and make sense of the data, and, ultimately, use the data to create meaningful impact all influence the success of using patient-reported outcomes. OBJECTIVES: The primary objective is to assess post-operative pain experiences by dental procedure type through 21 days post-procedure as reported by patients following dental procedures and assess patients' satisfaction with pain management following dental surgical procedures. Secondary objectives are to: 1) assess post-operative pain management strategies 1 week following dental surgical procedures, as recommended by practitioners and reported by patients, and 2) evaluate practitioner and patient acceptance of the FollowApp.Care post visit patient monitoring technology (FollowApp.Care). We will evaluate FollowApp.Care usage, perceived usefulness, ease of use, and impact on clinical workload. DESIGN AND METHODS: We describe the protocol for an observational study involving the use of the FollowApp.Care platform, an innovative mobile application that collects dental patients' assessments of their post-operative symptoms (e.g., pain). The study will be conducted in collaboration with the National Dental Practice-based Research Network, a collective Network of dental practices that include private and group practices, public health clinics, community health centers and Federal Qualified Health Centers, academic institutional settings, and special patient populations. We will recruit a minimum of 150 and up to 215 dental providers and up to 3147 patients who will receive push notifications through text messages FollowApp.Care on their mobile phones at designated time intervals following dental procedures. This innovative approach of implementing an existing and tested mobile health system technology into the real-world dental office setting will actively track pain and other complications following dental procedures. Through patients' use of their mobile phones, we expect to promptly and precisely identify specific pain levels and other issues after surgical dental procedures. The study's primary outcome will be the patients' reported pain experiences. Secondary outcomes include pain management strategies and medications implemented by the patient and provider and perceptions of usefulness and ease of use by patients and providers.


Assuntos
Telefone Celular , Envio de Mensagens de Texto , Humanos , Satisfação do Paciente , Dor Pós-Operatória/etiologia , Odontologia , Estudos Observacionais como Assunto
19.
J Am Dent Assoc ; 153(10): 996-1004, 2022 10.
Artigo em Inglês | MEDLINE | ID: mdl-35970673

RESUMO

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.


Assuntos
Sistema de Aprendizagem em Saúde , Doenças Periodontais , Periodontite , Perda de Dente , Informática Odontológica , Humanos , Doenças Periodontais/complicações , Doenças Periodontais/epidemiologia , Doenças Periodontais/prevenção & controle , Saúde da População , Perda de Dente/epidemiologia , Perda de Dente/prevenção & controle
20.
J Patient Saf ; 18(5): 470-474, 2022 08 01.
Artigo em Inglês | MEDLINE | ID: mdl-35948296

RESUMO

BACKGROUND: To achieve high-quality health care, adverse events (AEs) must be proactively recognized and mitigated. However, there is often ambiguity in applying guidelines and definitions. We describe the iterative calibration process needed to achieve a shared definition of AEs in dentistry. Our alignment process includes both independent and consensus building approaches. OBJECTIVE: We explore the process of defining dental AEs and the steps necessary to achieve alignment across different care providers. METHODS: Teams from 4 dental institutions across the United States iteratively reviewed patient records after identification of charts using an automated trigger tool. Calibration across teams was supported through negotiated definition of AEs and standardization of evidence provided in review. Interrater reliability was assessed using descriptive and κ statistics. RESULTS: After 5 iterative cycles of calibration, the teams (n = 8 raters) identified 118 cases. The average percent agreement for AE determination was 82.2%. Furthermore, the average, pairwise prevalence and bias-adjusted κ (PABAK) was 57.5% (κ = 0.575) for determining AE presence. The average percent agreement for categorization of the AE type was 78.5%, whereas the PABAK was 48.8%. Lastly, the average percent agreement for categorization of AE severity was 82.2% and the corresponding PABAK was 71.7%. CONCLUSIONS: Successful calibration across reviewers is possible after consensus building procedures. Higher levels of agreement were found when categorizing severity (of identified events) rather than the events themselves. Our results demonstrate the need for collaborative procedures as well as training for the identification and severity rating of AEs.


Assuntos
Odontologia , Consenso , Humanos , Reprodutibilidade dos Testes , Estados Unidos
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