Identifying subtypes of type 2 diabetes mellitus with machine learning: development, internal validation, prognostic validation and medication burden in linked electronic health records in 420 448 individuals.

INTRODUCTION: None of the studies of type 2 diabetes (T2D) subtyping to date have used linked population-level data for incident and prevalent T2D, incorporating a diverse set of variables, explainable methods for cluster characterization, or adhered to an established framework. We aimed to develop and validate machine learning (ML)-informed subtypes for type 2 diabetes mellitus (T2D) using nationally representative data. RESEARCH DESIGN AND METHODS: In population-based electronic health records (2006-2020; Clinical Practice Research Datalink) in individuals ≥18 years with incident T2D (n=420 448), we included factors (n=3787), including demography, history, examination, biomarkers and medications. Using a published framework, we identified subtypes through nine unsupervised ML methods (K-means, K-means++, K-mode, K-prototype, mini-batch, agglomerative hierarchical clustering, Birch, Gaussian mixture models, and consensus clustering). We characterized clusters using intracluster distributions and explainable artificial intelligence (AI) techniques. We evaluated subtypes for (1) internal validity (within dataset; across methods); (2) prognostic validity (prediction for 5-year all-cause mortality, hospitalization and new chronic diseases); and (3) medication burden. RESULTS: Development: We identified four T2D subtypes: metabolic, early onset, late onset and cardiometabolic. Internal validity: Subtypes were predicted with high accuracy (F1 score >0.98). Prognostic validity: 5-year all-cause mortality, hospitalization, new chronic disease incidence and medication burden differed across T2D subtypes. Compared with the metabolic subtype, 5-year risks of mortality and hospitalization in incident T2D were highest in late-onset subtype (HR 1.95, 1.85-2.05 and 1.66, 1.58-1.75) and lowest in early-onset subtype (1.18, 1.11-1.27 and 0.85, 0.80-0.90). Incidence of chronic diseases was highest in late-onset subtype and lowest in early-onset subtype. Medications: Compared with the metabolic subtype, after adjusting for age, sex, and pre-T2D medications, late-onset subtype (1.31, 1.28-1.35) and early-onset subtype (0.83, 0.81-0.85) were most and least likely, respectively, to be prescribed medications within 5 years following T2D onset. CONCLUSIONS: In the largest study using ML to date in incident T2D, we identified four distinct subtypes, with potential future implications for etiology, therapeutics, and risk prediction.

Use of electronic health record data to examine administrations of pro re nata analgesics during hip fracture post-acute care.

PURPOSE: Medications prescribed to older adults in US skilled nursing facilities (SNF) and administrations of pro re nata (PRN) "as needed" medications are unobservable in Medicare insurance claims. There is an ongoing deficit in our understanding of medication use during post-acute care. Using SNF electronic health record (EHR) datasets, including medication orders and barcode medication administration records, we described patterns of PRN analgesic prescribing and administrations among SNF residents with hip fracture. METHODS: Eligible participants resided in SNFs owned by 11 chains, had a diagnosis of hip fracture between January 1, 2018 to August 2, 2021, and received at least one administration of an analgesic medication in the 100 days after the hip fracture. We described the scheduling of analgesics, the proportion of available PRN doses administered, and the proportion of days with at least one PRN analgesic administration. RESULTS: Among 24 038 residents, 57.3% had orders for PRN acetaminophen, 67.4% PRN opioids, 4.2% PRN non-steroidal anti-inflammatory drugs, and 18.6% PRN combination products. The median proportion of available PRN doses administered per drug was 3%-50% and the median proportion of days where one or more doses of an ordered PRN analgesic was administered was 25%-75%. Results differed by analgesic class and the number of administrations ordered per day. CONCLUSIONS: EHRs can be leveraged to ascertain precise analgesic exposures during SNF stays. Future pharmacoepidemiology studies should consider linking SNF EHRs to insurance claims to construct a longitudinal history of medication use and healthcare utilization prior to and during episodes of SNF care.

Enhancing post-traumatic stress disorder patient assessment: leveraging natural language processing for research of domain criteria identification using electronic medical records.

BACKGROUND: Extracting research of domain criteria (RDoC) from high-risk populations like those with post-traumatic stress disorder (PTSD) is crucial for positive mental health improvements and policy enhancements. The intricacies of collecting, integrating, and effectively leveraging clinical notes for this purpose introduce complexities. METHODS: In our study, we created a natural language processing (NLP) workflow to analyze electronic medical record (EMR) data and identify and extract research of domain criteria using a pre-trained transformer-based natural language model, all-mpnet-base-v2. We subsequently built dictionaries from 100,000 clinical notes and analyzed 5.67 million clinical notes from 38,807 PTSD patients from the University of Pittsburgh Medical Center. Subsequently, we showcased the significance of our approach by extracting and visualizing RDoC information in two use cases: (i) across multiple patient populations and (ii) throughout various disease trajectories. RESULTS: The sentence transformer model demonstrated high F1 macro scores across all RDoC domains, achieving the highest performance with a cosine similarity threshold value of 0.3. This ensured an F1 score of at least 80% across all RDoC domains. The study revealed consistent reductions in all six RDoC domains among PTSD patients after psychotherapy. We found that 60.6% of PTSD women have at least one abnormal instance of the six RDoC domains as compared to PTSD men (51.3%), with 45.1% of PTSD women with higher levels of sensorimotor disturbances compared to men (41.3%). We also found that 57.3% of PTSD patients have at least one abnormal instance of the six RDoC domains based on our records. Also, veterans had the higher abnormalities of negative and positive valence systems (60% and 51.9% of veterans respectively) compared to non-veterans (59.1% and 49.2% respectively). The domains following first diagnoses of PTSD were associated with heightened cue reactivity to trauma, suicide, alcohol, and substance consumption. CONCLUSIONS: The findings provide initial insights into RDoC functioning in different populations and disease trajectories. Natural language processing proves valuable for capturing real-time, context dependent RDoC instances from extensive clinical notes.

Preparing Future Pediatric Care Providers with a Clinical Informatics Elective.

BACKGROUND: Clinical informatics (CI) has reshaped how medical information is shared, evaluated, and utilized in health care delivery. The widespread integration of electronic health records (EHRs) mandates proficiency among physicians and practitioners, yet medical trainees face a scarcity of opportunities for CI education. OBJECTIVES: We developed a CI rotation at a tertiary pediatric care center to teach categorical pediatric, pediatric-neurology, and medicine-pediatric residents foundational CI knowledge and applicable EHR skills. METHODS: Created in 2017 and redesigned in 2020, a CI rotation aimed to provide foundational CI knowledge, promote longitudinal learning, and encourage real-world application of CI skills/tools. Led by a team of five physician informaticist faculty, the curriculum offers personalized rotation schedules and individual sessions with faculty for each trainee. Trainees were tasked with completing an informatics project, knowledge assessment, and self-efficacy perception survey before and after rotation. Paired t-test analyses were used to compare pre- and postcurriculum perception survey. RESULTS: Thirty-one residents have completed the elective with their projects contributing to diverse areas such as medical education, division-specific initiatives, documentation improvement, regulatory compliance, and operating plan goals. The mean knowledge assessment percentage score increased from 77% (11.6) to 92% (10.6; p ≤ 0.05). Residents' perception surveys demonstrated improved understanding and confidence across various informatics concepts and tools (p ≤ 0.05). CONCLUSION: Medical trainees are increasingly interested in CI education and find it valuable. Our medical education curriculum was successful at increasing residents' understanding, self-efficacy, and confidence in utilizing CI concepts and EHR tools. Future data are needed to assess the impact such curricula have on graduates' proficiency and efficiency in the use of CI tools in the clinical workplace.

Undercounting diagnoses in Australian general practice: a data quality study with implications for population health reporting.

BACKGROUND: Diagnosis can often be recorded in electronic medical records (EMRs) as free-text or using a term with a diagnosis code. Researchers, governments, and agencies, including organisations that deliver incentivised primary care quality improvement programs, frequently utilise coded data only and often ignore free-text entries. Diagnosis data are reported for population healthcare planning including resource allocation for patient care. This study sought to determine if diagnosis counts based on coded diagnosis data only, led to under-reporting of disease prevalence and if so, to what extent for six common or important chronic diseases. METHODS: This cross-sectional data quality study used de-identified EMR data from 84 general practices in Victoria, Australia. Data represented 456,125 patients who attended one of the general practices three or more times in two years between January 2021 and December 2022. We reviewed the percentage and proportional difference between patient counts of coded diagnosis entries alone and patient counts of clinically validated free-text entries for asthma, chronic kidney disease, chronic obstructive pulmonary disease, dementia, type 1 diabetes and type 2 diabetes. RESULTS: Undercounts were evident in all six diagnoses when using coded diagnoses alone (2.57-36.72% undercount), of these, five were statistically significant. Overall, 26.4% of all patient diagnoses had not been coded. There was high variation between practices in recording of coded diagnoses, but coding for type 2 diabetes was well captured by most practices. CONCLUSION: In Australia clinical decision support and the reporting of aggregated patient diagnosis data to government that relies on coded diagnoses can lead to significant underreporting of diagnoses compared to counts that also incorporate clinically validated free-text diagnoses. Diagnosis underreporting can impact on population health, healthcare planning, resource allocation, and patient care. We propose the use of phenotypes derived from clinically validated text entries to enhance the accuracy of diagnosis and disease reporting. There are existing technologies and collaborations from which to build trusted mechanisms to provide greater reliability of general practice EMR data used for secondary purposes.

Excess burden of respiratory and abdominal conditions following COVID-19 infections during the ancestral and Delta variant periods in the United States: An EHR-based cohort study from the RECOVER program.

IMPORTANCE: The frequency and characteristics of post-acute sequelae of SARS-CoV-2 infection (PASC) may vary by SARS-CoV-2 variant. OBJECTIVE: To characterize PASC-related conditions among individuals likely infected by the ancestral strain in 2020 and individuals likely infected by the Delta variant in 2021. DESIGN: Retrospective cohort study of electronic medical record data for approximately 27 million patients from March 1, 2020-November 30, 2021. SETTING: Healthcare facilities in New York and Florida. PARTICIPANTS: Patients who were at least 20 years old and had diagnosis codes that included at least one SARS-CoV-2 viral test during the study period. EXPOSURE: Laboratory-confirmed COVID-19 infection, classified by the most common variant prevalent in those regions at the time. MAIN OUTCOME(S) AND MEASURE(S): Relative risk (estimated by adjusted hazard ratio [aHR]) and absolute risk difference (estimated by adjusted excess burden) of new conditions, defined as new documentation of symptoms or diagnoses, in persons between 31-180 days after a positive COVID-19 test compared to persons without a COVID-19 test or diagnosis during the 31-180 days after the last negative test. RESULTS: We analyzed data from 560,752 patients. The median age was 57 years; 60.3% were female, 20.0% non-Hispanic Black, and 19.6% Hispanic. During the study period, 57,616 patients had a positive SARS-CoV-2 test; 503,136 did not. For infections during the ancestral strain period, pulmonary fibrosis, edema (excess fluid), and inflammation had the largest aHR, comparing those with a positive test to those without a COVID-19 test or diagnosis (aHR 2.32 [95% CI 2.09 2.57]), and dyspnea (shortness of breath) carried the largest excess burden (47.6 more cases per 1,000 persons). For infections during the Delta period, pulmonary embolism had the largest aHR comparing those with a positive test to a negative test (aHR 2.18 [95% CI 1.57, 3.01]), and abdominal pain carried the largest excess burden (85.3 more cases per 1,000 persons). CONCLUSIONS AND RELEVANCE: We documented a substantial relative risk of pulmonary embolism and a large absolute risk difference of abdomen-related symptoms after SARS-CoV-2 infection during the Delta variant period. As new SARS-CoV-2 variants emerge, researchers and clinicians should monitor patients for changing symptoms and conditions that develop after infection.

Relationship between youth cardiometabolic health and physical activity in medical records.

BACKGROUND: Thers is limited research examining modifiable cardiometabolic risk factors with a single-item health behavior question obtained during a clinic visit. Such information could support clinicians in identifying patients at risk for adverse cardiometabolic health. We investigated if children meeting physical activity or screen time recommendations, collected during clinic visits, have better cardiometabolic health than children not meeting recommendations. We hypothesized that children meeting either recommendation would have fewer cardiometabolic risk factors. METHODS AND FINDINGS: This cross-sectional study used data from electronic medical records (EMRs) between January 1, 2013 through December 30, 2017 from children (2-18 years) with a well child visits and data for ≥1 cardiometabolic risk factor (i.e., systolic and diastolic blood pressure, glycated hemoglobin, alanine transaminase, high-density and low-density lipoprotein, total cholesterol, and/or triglycerides). Physical activity and screen time were patient/caregiver-reported. Analyses included EMRs from 63,676 well child visits by 30,698 unique patients (49.3% female; 41.7% Black, 31.5% Hispanic). Models that included data from all visits indicated children meeting physical activity recommendations had reduced risk for abnormal blood pressure (odds ratio [OR] = 0.91, 95%CI 0.86, 0.97; p = 0.002), glycated hemoglobin (OR = 0.83, 95%CI 0.75, 0.91; p = 0.00006), alanine transaminase (OR = 0.85, 95%CI 0.79, 0.92; p = 0.00001), high-density lipoprotein (OR = 0.88, 95%CI 0.82, 0.95; p = 0.0009), and triglyceride values (OR = 0.89, 95%CI 0.83, 0.96; p = 0.002). Meeting screen time recommendations was not associated with abnormal cardiometabolic risk factors. CONCLUSION: Collecting information on reported adherence to meeting physical activity recommendations can provide clinicians with additional information to identify patients with a higher risk of adverse cardiometabolic health.

Leveraging Electronic Health Records to Assess Residential Mobility Among Veterans in the Veterans Health Administration.

BACKGROUND: Residential mobility, or a change in residence, can influence health care utilization and outcomes. Health systems can leverage their patients' residential addresses stored in their electronic health records (EHRs) to better understand the relationships among patients' residences, mobility, and health. The Veteran Health Administration (VHA), with a unique nationwide network of health care systems and integrated EHR, holds greater potential for examining these relationships. METHODS: We conducted a cross-sectional analysis to examine the association of sociodemographics, clinical conditions, and residential mobility. We defined residential mobility by the number of VHA EHR residential addresses identified for each patient in a 1-year period (1/1-12/31/2018), with 2 different addresses indicating one move. We used generalized logistic regression to model the relationship between a priori selected correlates and residential mobility as a multinomial outcome (0, 1, ≥2 moves). RESULTS: In our sample, 84.4% (n=3,803,475) veterans had no move, 13.0% (n=587,765) had 1 move, and 2.6% (n=117,680) had ≥2 moves. In the multivariable analyses, women had greater odds of moving [aOR=1.11 (95% CI: 1.10,1.12) 1 move; 1.27 (1.25,1.30) ≥2 moves] than men. Veterans with substance use disorders also had greater odds of moving [aOR=1.26 (1.24,1.28) 1 move; 1.77 (1.72,1.81) ≥2 moves]. DISCUSSION: Our study suggests about 16% of veterans seen at VHA had at least 1 residential move in 2018. VHA data can be a resource to examine relationships between place, residential mobility, and health.

Lung Cancer Screening Before and After a Multifaceted Electronic Health Record Intervention: A Nonrandomized Controlled Trial.

Importance: Lung cancer is the deadliest cancer in the US. Early-stage lung cancer detection with lung cancer screening (LCS) through low-dose computed tomography (LDCT) improves outcomes. Objective: To assess the association of a multifaceted clinical decision support intervention with rates of identification and completion of recommended LCS-related services. Design, Setting, and Participants: This nonrandomized controlled trial used an interrupted time series design, including 3 study periods from August 24, 2019, to April 27, 2022: baseline (12 months), period 1 (11 months), and period 2 (9 months). Outcome changes were reported as shifts in the outcome level at the beginning of each period and changes in monthly trend (ie, slope). The study was conducted at primary care and pulmonary clinics at a health care system headquartered in Salt Lake City, Utah, among patients aged 55 to 80 years who had smoked 30 pack-years or more and were current smokers or had quit smoking in the past 15 years. Data were analyzed from September 2023 through February 2024. Interventions: Interventions in period 1 included clinician-facing preventive care reminders, an electronic health record-integrated shared decision-making tool, and narrative LCS guidance provided in the LDCT ordering screen. Interventions in period 2 included the same clinician-facing interventions and patient-facing reminders for LCS discussion and LCS. Main Outcome and Measure: The primary outcome was LCS care gap closure, defined as the identification and completion of recommended care services. LCS care gap closure could be achieved through LDCT completion, other chest CT completion, or LCS shared decision-making. Results: The study included 1865 patients (median [IQR] age, 64 [60-70] years; 759 female [40.7%]). The clinician-facing intervention (period 1) was not associated with changes in level but was associated with an increase in slope of 2.6 percentage points (95% CI, 2.4-2.7 percentage points) per month in care gap closure through any means and 1.6 percentage points (95% CI, 1.4-1.8 percentage points) per month in closure through LDCT. In period 2, introduction of patient-facing reminders was associated with an immediate increase in care gap closure (2.3 percentage points; 95% CI, 1.0-3.6 percentage points) and closure through LDCT (2.4 percentage points; 95% CI, 0.9-3.9 percentage points) but was not associated with an increase in slope. The overall care gap closure rate was 175 of 1104 patients (15.9%) at the end of the baseline period vs 588 of 1255 patients (46.9%) at the end of period 2. Conclusions and Relevance: In this study, a multifaceted intervention was associated with an improvement in LCS care gap closure. Trial Registration: ClinicalTrials.gov Identifier: NCT04498052.

Association of post-COVID phenotypic manifestations with new-onset psychiatric disease.

Acute COVID-19 infection can be followed by diverse clinical manifestations referred to as Post Acute Sequelae of SARS-CoV2 Infection (PASC). Studies have shown an increased risk of being diagnosed with new-onset psychiatric disease following a diagnosis of acute COVID-19. However, it was unclear whether non-psychiatric PASC-associated manifestations (PASC-AMs) are associated with an increased risk of new-onset psychiatric disease following COVID-19. A retrospective electronic health record (EHR) cohort study of 2,391,006 individuals with acute COVID-19 was performed to evaluate whether non-psychiatric PASC-AMs are associated with new-onset psychiatric disease. Data were obtained from the National COVID Cohort Collaborative (N3C), which has EHR data from 76 clinical organizations. EHR codes were mapped to 151 non-psychiatric PASC-AMs recorded 28-120 days following SARS-CoV-2 diagnosis and before diagnosis of new-onset psychiatric disease. Association of newly diagnosed psychiatric disease with age, sex, race, pre-existing comorbidities, and PASC-AMs in seven categories was assessed by logistic regression. There were significant associations between a diagnosis of any psychiatric disease and five categories of PASC-AMs with odds ratios highest for neurological, cardiovascular, and constitutional PASC-AMs with odds ratios of 1.31, 1.29, and 1.23 respectively. Secondary analysis revealed that the proportions of 50 individual clinical features significantly differed between patients diagnosed with different psychiatric diseases. Our study provides evidence for association between non-psychiatric PASC-AMs and the incidence of newly diagnosed psychiatric disease. Significant associations were found for features related to multiple organ systems. This information could prove useful in understanding risk stratification for new-onset psychiatric disease following COVID-19. Prospective studies are needed to corroborate these findings.

Speed and Accuracy of Recording Medication History by Pharmacists in Secondary Emergency Rooms.

In Japan, only few hospitals have pharmacists in their secondary emergency rooms to record medication history and provide drug information in real time. In this study, we investigated the benefits of pharmacist intervention in secondary emergency rooms by comparing the time taken by the pharmacists and non-pharmacists in the emergency room to record the medication history in the electronic medical record and the accuracy of its content. The study period was from September 1 to September 30, 2022, and included patients who were transported to our hospital for emergency care between 9:00 and 16:30. We compared the time taken between the patient's arrival until the recording of their medication history and the accuracy of the record by the emergency room pharmacists and non-pharmacists (paramedics or medical clerks). The study included 58 patients whose medication histories were collected by pharmacists, and 11 patients whose histories were collected by non-pharmacists. For pharmacists, the median time to record medication history in the electronic medical record was 12 min, whereas for non-pharmacists, it was 19 min, which was significantly different (p=0.015). The pharmacists accurately recorded the medication history of 98.3% (57/58) of patients, whereas non-pharmacists accurately recorded it for only 54.5% (6/11) of patients, with a significant difference (p<0.01). We observed that in secondary emergency rooms, when pharmacists were responsible for recording the patients' medication histories, it resulted in rapid and accurate sharing of medication history.

[The future of hematopoietic stem cell transplantation using DX].

The advancement of information and communication technology (ICT) is bringing significant changes to hematopoietic stem cell transplantation. Digital transformation (DX) simplifies the collection and analysis of medical data, enabling provision of medical services beyond geographical constraints through telemedicine. Convenient access to electronic medical records and vital data from wearable devices could facilitate personalized medicine, for example, by predicting of disease onset. Online consultations are effective in improving the efficiency of posttransplant follow-ups, donor recruitment, and donor screening in rural areas. Moreover, patient-reported outcomes are effective in improving treatment outcomes and patient management. The effective utilization of ICT necessitates the enhancement of information technology (IT) literacy among healthcare professionals and patients, as well as development of IT proficiency among medical personnel. DX in hematopoietic stem cell transplantation contributes to the improvement of treatment outcomes, quality of medical care, and patient convenience while introducing new possibilities for the future of healthcare.

Optimising the use of electronic medical records for large scale research in psychiatry.

The explosion and abundance of digital data could facilitate large-scale research for psychiatry and mental health. Research using so-called "real world data"-such as electronic medical/health records-can be resource-efficient, facilitate rapid hypothesis generation and testing, complement existing evidence (e.g. from trials and evidence-synthesis) and may enable a route to translate evidence into clinically effective, outcomes-driven care for patient populations that may be under-represented. However, the interpretation and processing of real-world data sources is complex because the clinically important 'signal' is often contained in both structured and unstructured (narrative or "free-text") data. Techniques for extracting meaningful information (signal) from unstructured text exist and have advanced the re-use of routinely collected clinical data, but these techniques require cautious evaluation. In this paper, we survey the opportunities, risks and progress made in the use of electronic medical record (real-world) data for psychiatric research.

Leveraging Electronic Health Record Data to Understand Gaps Underlying the Underdiagnosis of Lynch Syndrome.

Using the electronic health record to address the underdiagnosis of Lynch syndrome.

Population-Level Identification of Patients With Lynch Syndrome for Clinical Care, Quality Improvement, and Research.

PURPOSE: Identification of those at risk of hereditary cancer syndromes using electronic health record (EHR) data sources is important for clinical care, quality improvement, and research. We describe diagnostic processes, previously seldom reported, for a common hereditary cancer syndrome, Lynch syndrome (LS), using EHR data within a community-based, multicenter, demographically diverse health system. METHODS: Within a retrospective cohort enrolled between 2015 and 2020 at Kaiser Permanente Northern California, we assessed electronic diagnostic domains for LS including (1) family history of LS-associated cancer; (2) personal history of LS-associated cancer; (3) LS screening via mismatch repair deficiency (MMRD) testing of newly diagnosed malignancy; (4) germline genetic test results; and (5) clinician-entered diagnostic codes for LS. We calculated proportions and overlap for each diagnostic domain descriptively. RESULTS: Among 5.8 million individuals, (1) 28,492 (0.49%) had a family history of LS-associated cancer of whom 3,635 (13%) underwent genetic testing; (2) 100,046 (1.7%) had a personal history of a LS-associated cancer; and (3) 8,711 (0.1%) were diagnosed with colorectal cancer of whom 7,533 (86%) underwent MMRD screening and of the positive screens (486), 130 (27%) underwent germline testing. One thousand seven hundred and fifty-seven (0.03%) were diagnosed with endometrial cancer of whom 1,613 (92%) underwent MMRD screening and of the 195 who screened positive, 55 (28%) underwent genetic testing. (4) 30,790 (0.05%) had LS germline genetic testing with 707 (0.01%) testing positive; and (5) 1,273 (0.02%) had a clinician-entered diagnosis of LS. CONCLUSION: It is feasible to electronically characterize the diagnostic processes of LS. No single data source comprehensively identifies all LS carriers. There is underutilization of LS genetic testing for those eligible and underdiagnosis of LS. Our work informs similar efforts in other settings for hereditary cancer syndromes.

Effect of integration of prescription drug monitoring program data in the electronic health record on queries by primary care providers.

Objective: To evaluate the impact of PDMP integration in the EHR on provider query rates within twelve primary care clinics in one academic medical center. Methods: Using linked data from the EHR and state PDMP program, we evaluated changes in PDMP query rates using a stepped-wedge observational design where integration was implemented in three waves (four clinics per wave) over a five-month period (May, July, September 2019). Multivariable negative binomial general estimating equations (GEE) models assessed changes in PDMP query rates, overall and across several provider and clinic-level subgroups. Results: Among 206 providers in PDMP integrated clinics, the average number of queries per provider per month increased significantly from 1.43 (95% CI 1.07 - 1.91) pre-integration to 3.94 (95% CI 2.96 - 5.24) post-integration, a 2.74-fold increase (95% CI 2.11 to 3.59; p < .0001). Those in the lowest quartile of PDMP use pre-integration increased 36.8-fold (95% CI 16.91 - 79.95) after integration, significantly more than other pre-integration PDMP use quartiles. Conclusions: Integration of the PDMP in the EHR significantly increased the use of the PDMP overall and across all studied subgroups. PDMP use increased to a greater degree among providers with lower PDMP use pre-integration.

Peril and Promise: Teens, Tech, and America's Mental Health Crisis.

Advances in Internet technologies have implications for the health and development of children and adolescents with potential for both beneficial and harmful outcomes. Similar technological advances also impact how psychiatrists deliver mental health care in clinical settings. Internet tech adds complexities to psychiatric practice in the form of electronic health records, patient portals, and virtual patient contact, which clinicians must understand and successfully incorporate into practice. Digital therapeutics and virtual mental health endeavors offer new treatment delivery options for patients and providers. Some have proven benefits, such as improved accessibility for patients, but all require provider expertise to utilize.

MSGene: a multistate model using genetic risk and the electronic health record applied to lifetime risk of coronary artery disease.

Coronary artery disease (CAD) is the leading cause of death among adults worldwide. Accurate risk stratification can support optimal lifetime prevention. Current methods lack the ability to incorporate new information throughout the life course or to combine innate genetic risk factors with acquired lifetime risk. We designed a general multistate model (MSGene) to estimate age-specific transitions across 10 cardiometabolic states, dependent on clinical covariates and a CAD polygenic risk score. This model is designed to handle longitudinal data over the lifetime to address this unmet need and support clinical decision-making. We analyze longitudinal data from 480,638 UK Biobank participants and compared predicted lifetime risk with the 30-year Framingham risk score. MSGene improves discrimination (C-index 0.71 vs 0.66), age of high-risk detection (C-index 0.73 vs 0.52), and overall prediction (RMSE 1.1% vs 10.9%), in held-out data. We also use MSGene to refine estimates of lifetime absolute risk reduction from statin initiation. Our findings underscore our multistate model's potential public health value for accurate lifetime CAD risk estimation using clinical factors and increasingly available genetics toward earlier more effective prevention.

Using 42 CFR part 2 revisions to integrate substance use disorder treatment information into electronic health records at a safety net health system.

BACKGROUND: Regulations put in place to protect the privacy of individuals receiving substance use disorder (SUD) treatment have resulted in an unintended consequence of siloed SUD treatment and referral information outside of the integrated electronic health record (EHR). Recent revisions to these regulations have opened the door to data integration, which creates opportunities for enhanced patient care and more efficient workflows. We report on the experience of one safety-net hospital system integrating SUD treatment data into the EHR. METHODS: SUD treatment and referral information was integrated from siloed systems into the EHR through the implementation of a referral order, treatment episode definition, and referral and episode-related tools for addiction therapists and other clinicians. Integration was evaluated by monitoring SUD treatment episode characteristics, patient characteristics, referral linkage, and treatment episode retention before and after integration. Satisfaction of end-users with the new tools was evaluated through a survey of addiction therapists. RESULTS: After integration, three more SUD treatment programs were represented in the EHR. This increased the number of patients that could be tracked as initiating SUD treatment by 250%, from 562 before to 1,411 after integration. After integration, overall referral linkage declined (74% vs. 48%) and treatment episode retention at 90-days was higher (45% vs. 74%). Addiction therapists appreciated the efficiency of having all SUD treatment information in the EHR but did not find that the tools provided a large time savings shortly after integration. CONCLUSIONS: Integration of SUD treatment program data into the EHR facilitated both care coordination in patient treatment and quality improvement initiatives for treatment programs. Referral linkage and retention rates were likely modified by a broader capture of patients and changed outcome definition criteria. Greater preparatory workflow analysis may decrease initial end-user burden. Integration of siloed data, made possible given revised regulations, is essential to an efficient hub-and-spoke model of care, which must standardize and coordinate patient care across multiple clinics and departments.