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Background: Clinician perceptions before and after inviting patients to read office notes (open notes) are unknown., Objective: To describe changes in clinicians' attitudes about sharing notes with patients., Design, Participants, and Main Measure: Survey of outpatient primary and specialty care clinicians who were from a large group practice and had one or more patients who accessed notes. The main outcome was percent change (before vs. after implementation) in clinician perception that online visit notes are beneficial overall., Key Results: Of the 563 invited clinicians, 400 (71%) took the baseline survey; 295 were eligible for a follow-up survey with 192 (65%) responding (119 primary care, 47 medical specialties, 26 surgical specialties). Before implementation, 29% agreed or somewhat agreed that visit notes online are beneficial overall, increasing to 71% following implementation (p<0.001); 44% switched beliefs from bad to good idea; and 2% reported the opposite change (p<0.001). This post-implementation change was observed in all clinician categories. Compared to pre-implementation, fewer clinicians had concerns about office visits taking longer (47% pre vs. 15% post) or requiring more time for questions (71% vs. 16%), or producing notes (57% vs. 28%). Before and after implementation, most clinicians reported being less candid in documentation (65% vs. 52%) and that patients would have more control of their care (72% vs. 78%) and worry more (72% vs. 65%)., Conclusions: Following implementation, more primary and specialty care clinicians agreed that sharing notes with patients online was beneficial overall. Fewer had concerns about more time needed for office visits or documentation. Most thought patients would worry more and reported being less candid in documentation., (© 2021. Society of General Internal Medicine.)

Objective: Guidelines recommend against prescribing antipsychotics without an indication, yet prescription rates remain undesirably high for youths. Information technology can facilitate guideline-based prescribing, but little is known about providers' needs. The Safer Use of Antipsychotics in Youth project is implementing an algorithm-based workflow for peer consultation, care navigation, and expedited psychotherapy access. To optimize workflow for a multisite trial, we engaged providers for input., Methods: Guided by human-centered design, we interviewed 15 providers from Kaiser Permanente Washington and Nationwide Children's Hospital about their prescribing barriers and workflow preferences. We identified qualitative themes on barriers affecting implementation and design opportunities to optimize workflow., Results: Providers expressed two major barriers: potential disruptions to clinical practice and threats to professional autonomy. Three design opportunities emerged: à la carte orders, passive review of orders, and consultation self-acknowledgment., Conclusions: Human-centered design offers an innovative approach to improve guideline-based prescribing with optimizations that are grounded in providers' needs.

Objective: The authors sought to develop and validate models using electronic health records to predict suicide attempt and suicide death following an outpatient visit., Method: Across seven health systems, 2,960,929 patients age 13 or older (mean age, 46 years; 62% female) made 10,275,853 specialty mental health visits and 9,685,206 primary care visits with mental health diagnoses between Jan. 1, 2009, and June 30, 2015. Health system records and state death certificate data identified suicide attempts (N=24,133) and suicide deaths (N=1,240) over 90 days following each visit. Potential predictors included 313 demographic and clinical characteristics extracted from records for up to 5 years before each visit: prior suicide attempts, mental health and substance use diagnoses, medical diagnoses, psychiatric medications dispensed, inpatient or emergency department care, and routinely administered depression questionnaires. Logistic regression models predicting suicide attempt and death were developed using penalized LASSO (least absolute shrinkage and selection operator) variable selection in a random sample of 65% of the visits and validated in the remaining 35%., Results: Mental health specialty visits with risk scores in the top 5% accounted for 43% of subsequent suicide attempts and 48% of suicide deaths. Of patients scoring in the top 5%, 5.4% attempted suicide and 0.26% died by suicide within 90 days. C-statistics (equivalent to area under the curve) for prediction of suicide attempt and suicide death were 0.851 (95% CI=0.848, 0.853) and 0.861 (95% CI=0.848, 0.875), respectively. Primary care visits with scores in the top 5% accounted for 48% of subsequent suicide attempts and 43% of suicide deaths. C-statistics for prediction of suicide attempt and suicide death were 0.853 (95% CI=0.849, 0.857) and 0.833 (95% CI=0.813, 0.853), respectively., Conclusions: Prediction models incorporating both health record data and responses to self-report questionnaires substantially outperform existing suicide risk prediction tools.

Background: While clinicians are expected to routinely assess and address suicide risk, existing data provide little guidance regarding the significance of visit-to-visit changes in suicidal ideation., Methods: Electronic health records from four large healthcare systems identified patients completing the Patient Health Questionnaire or PHQ9 at outpatient visits. For patients completing two questionnaires within 90 days, health system records and state vital records were used to identify nonfatal and fatal suicide attempts. Analyses examined how changes in PHQ9 item 9 responses between visits predicted suicide attempt or suicide death over 90 days following the second visit., Results: Analyses included 430,701 pairs of item 9 responses for 118,696 patients. Among patients reporting thoughts of death or self-harm "nearly every day" at the first visit, risk of suicide attempt after the second visit ranged from approximately 2.0% among those reporting continued thoughts "nearly every day" down to 0.5% among those reporting a decrease to "not at all." Among those reporting thoughts of death or self-harm "not at all" at the first visit, risk of suicide attempt following the second visit ranged from approximately 0.2% among those continuing to report such thoughts "not at all" up to 1.2% among those reporting an increase to "nearly every day"., Conclusions: Resolution of suicidal ideation between visits does imply a clinically important reduction in short-term risk, but prior suicidal ideation still implies significant residual risk. Onset of suicidal ideation between visits does not imply any special elevation compared to ongoing suicidal ideation. Risk is actually highest for patients repeatedly reporting thoughts of death or self-harm., (© 2017 Wiley Periodicals, Inc.)

Objective: To examine the association between thoughts of death or self-harm reported on item 9 of the Patient Health Questionnaire (PHQ) depression module and the risk of suicide attempt or suicide death over the following 2 years., Method: In 4 health care systems participating in the Mental Health Research Network, electronic records identified 509,945 adult outpatients completing 1,228,308 PHQ depression questionnaires during visits to primary care, specialty mental health, and other outpatient providers between January 1, 2007 and December 31, 2012. 9,203 nonfatal suicide attempts were identified using health system records of inpatient or outpatient encounters for self-inflicted injury. 484 suicide deaths were identified using cause-of-death codes from state mortality data., Results: Cumulative hazard of suicide attempt during 2 years ranged from approximately 0.5% among those reporting thoughts of death or self-harm "not at all" to 3.5% among those reporting such thoughts "nearly every day." Cumulative hazard of suicide death during 2 years ranged from approximately 0.04% among those responding "not at all" to 0.19% among those responding "nearly every day." The excess hazard associated with thoughts of death or self-harm declined with time, but remained 2- to 5-fold higher for at least 18 months. Nevertheless, 39% of suicide attempts and 36% of suicide deaths within 30 days of completing a PHQ occurred among those responding "not at all" to item 9., Conclusions: In community practice, response to PHQ item 9 is a strong predictor of suicide attempt and suicide death over the following 2 years. For patients reporting thoughts of death or self-harm, suicide prevention efforts must address this enduring vulnerability., Competing Interests: The authors have no relevant financial interests to disclose., (© Copyright 2016 Physicians Postgraduate Press, Inc.)

Background: Stevens-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN) carry a high mortality risk. While identifying clinical and genetic risk factors for these conditions has been hindered by their rarity, large electronic health databases hold promise for identifying large numbers of cases for study, especially with the introduction in 2008 of ICD-9 codes more specific for these conditions., Objective: The objective of this study is to estimate the validity of ICD-9 codes for ascertaining SJS/TEN in 12 collaborating research units in the USA, covering almost 60 million lives., Methods: From the electronic databases at each site, we ascertained potential cases of SJS/TEN using ICD-9 codes. At five sites, a subset of medical records was abstracted and standardized criteria applied by board-certified dermatologists to adjudicate diagnoses. Multivariate logistic regression was used to identify factors independently associated with validated SJS/TEN cases., Results: A total of 56 591 potential cases of SJS/TEN were identified. A subset of 276 charts was selected for adjudication and 39 (of the 276) were confirmed as SJS/TEN. Patients with the ICD-9 codes introduced after 2008 were more likely to be confirmed as cases (OR 3.32; 95%CI 0.82, 13.47) than those identified in earlier years. Likelihood of case status increased with length of hospitalization. Applying the probability of case status to the 56 591 potential cases, we estimated 475-875 to be valid SJS/TEN cases., Conclusion: Newer ICD-9 codes, along with length of hospitalization, identified patients with a high likelihood of SJS/TEN. This is important for identification of subjects for future pharmacogenomics studies., (Copyright © 2015 John Wiley & Sons, Ltd.)

Background: Given the low usage of virtual health care prior to the COVID‐19 pandemic, it was unclear whether those living in rural locations would benefit from increased availability of virtual mental health care. The rapid transition to virtual services during the COVID‐19 pandemic allowed for a unique opportunity to examine how the transition to virtual mental health care impacted psychotherapy disruption (i.e., 45+ days between appointments) among individuals living in rural locations compared with those living in nonrural locations. Methods: Electronic health record and insurance claims data were collected from three health care systems in the United States including rurality status and psychotherapy disruption. Psychotherapy disruption was measured before and after the COVID‐19 pandemic onset. Results: Both the nonrural and rural cohorts had significant decreases in the rates of psychotherapy disruption from pre‐ to post‐COVID‐19 onset (32.5–16.0% and 44.7–24.8%, respectively, p < 0.001). The nonrural cohort had a greater reduction of in‐person visits compared with the rural cohort (96.6–45.0 vs. 98.0–66.2%, respectively, p < 0.001). Among the rural cohort, those who were younger and those with lower education had greater reductions in psychotherapy disruption rates from pre‐ to post‐COVID‐19 onset. Several mental health disorders were associated with experiencing psychotherapy disruption. Conclusions: Though the rapid transition to virtual mental health care decreased the rate of psychotherapy disruption for those living in rural locations, the reduction was less compared with nonrural locations. Other strategies are needed to improve psychotherapy disruption, especially among rural locations (i.e., telephone visits). [ABSTRACT FROM AUTHOR]

To estimate the cost of implementing a clinical program designed to support safer use of antipsychotics in children and adolescents (youth) age 3–17 years at the time of initiating an antipsychotic medication. We calculate the costs of implementing a psychiatric consultation and navigation program for youth prescribed antipsychotic medications across 4 health systems, which included an electronic health record (EHR) decision support tool, consultation with a child and adolescent psychiatrist, and up to 6 months of behavioral health care navigation, as well as telemental health for patients (n = 348). Cost data were collected for both start-up and ongoing intervention phases and are estimated over a 1-year period. Data sources included study records and time-in-motion reports, analyzed from a health system perspective. Costs included both labor and nonlabor costs (2019 US dollars). The average total start-up and ongoing costs per health system were $34,007 and $185,174, respectively. The average total cost per patient was $2,128. The highest average ongoing labor cost components were telemental health ($901 per patient), followed by child and adolescent psychiatrist consultation ($659), and the lowest cost component was primary care/behavioral health provider time to review/respond to the EHR decision support tool and case consultation ($24). For health systems considering programs to promote safer and targeted use of antipsychotics among youth, this study provides estimates of the full start-up and ongoing costs of an EHR decision support tool, psychiatric consultation service, and psychotherapeutic services for patients and families. Trial registration: Clinicaltrials.gov, NCT03448575 [ABSTRACT FROM AUTHOR]

This case-control study assesses disruptive life events of individuals with schizophrenia or bipolar I disorder using public consumer credit records in conjunction with electronic health record data. Key Points: Question: Can novel, publicly available data aggregated by a consumer credit reporting agency support population-level studies on the occurrence of disruptive life events for patients with diagnoses of bipolar I disorder (BPI) or schizophrenia? Findings: In this case-control study of 46 167 patients with a diagnosis of BPI or schizophrenia, electronic health records data and TransUnion data were used for arrests, bankruptcies, lien, and judgment filings and demonstrated differences in disruptive life events among patients with diagnoses of BPI or schizophrenia and 2 matched comparison cohorts. Meaning: TransUnion data can support population-level studies on the occurrence of disruptive life events and may support effectiveness studies that assess disruptive life events in addition to clinical outcomes. Importance: There is a dearth of population-level data on major disruptive life events (defined here as arrests by a legal authority, address changes, bankruptcy, lien, and judgment filings) for patients with bipolar I disorder (BPI) or schizophrenia, which has limited studies on mental health and treatment outcomes. Objective: To conduct a population-level study on disruptive life events by using publicly available data on disruptive life events, aggregated by a consumer credit reporting agency in conjunction with electronic health record (EHR) data. Design, Setting, and Participants: This study used EHR data from 2 large, integrated health care systems, Kaiser Permanente Southern California and Henry Ford Health. Cohorts of patients diagnosed from 2007 to 2019 with BPI or schizophrenia were matched 1:1 by age at analysis, age at diagnosis (if applicable), sex, race and ethnicity, and Medicaid status to (1) an active comparison group with diagnoses of major depressive disorder (MDD) and (2) a general health (GH) cohort without diagnoses of BPI, schizophrenia, or MDD. Patients with diagnoses of BPI or schizophrenia and their respective comparison cohorts were matched to public records data aggregated by a consumer credit reporting agency (98% match rate). Analysis took place between November 2020 and December 2022. Main Outcomes and Measures: The differences in the occurrence of disruptive life events among patients with BPI or schizophrenia and their comparison groups. Results: Of 46 167 patients, 30 008 (65%) had BPI (mean [SD] age, 42.6 [14.2] years) and 16 159 (35%) had schizophrenia (mean [SD], 41.4 [15.1] years). The majoriy of patients were White (30 167 [65%]). In addition, 18 500 patients with BPI (62%) and 6552 patients with schizophrenia (41%) were female. Patients with BPI were more likely to change addresses than patients in either comparison cohort (with the incidence ratio being as high as 1.25 [95% CI, 1.23-1.28]) when compared with GH cohort. Patients with BPI were also more likely to experience any of the financial disruptive life events with odds ratio ranging from 1.15 [95% CI, 1.07-1.24] to 1.50 [95% CI, 1.42-1.58]). The largest differences in disruptive life events were seen in arrests of patients with either BPI or schizophrenia compared with GH peers (3.27 [95% CI, 2.84-3.78] and 3.04 [95% CI, 2.57-3.59], respectively). Patients with schizophrenia had fewer address changes and were less likely to experience a financial event than their matched comparison cohorts. Conclusions and Relevance: This study demonstrated that data aggregated by a consumer credit reporting agency can support population-level studies on disruptive life events among patients with BPI or schizophrenia. [ABSTRACT FROM AUTHOR]

Suicide risk prediction models can identify individuals for targeted intervention. Discussions of transparency, explainability, and transportability in machine learning presume complex prediction models with many variables outperform simpler models. We compared random forest, artificial neural network, and ensemble models with 1500 temporally defined predictors to logistic regression models. Data from 25,800,888 mental health visits made by 3,081,420 individuals in 7 health systems were used to train and evaluate suicidal behavior prediction models. Model performance was compared across several measures. All models performed well (area under the receiver operating curve [AUC]: 0.794–0.858). Ensemble models performed best, but improvements over a regression model with 100 predictors were minimal (AUC improvements: 0.006–0.020). Results are consistent across performance metrics and subgroups defined by race, ethnicity, and sex. Our results suggest simpler parametric models, which are easier to implement as part of routine clinical practice, perform comparably to more complex machine learning methods. [ABSTRACT FROM AUTHOR]

Background: Few studies report on machine learning models for suicide risk prediction in adolescents and their utility in identifying those in need of further evaluation. This study examined whether a model trained and validated using data from all age groups works as well for adolescents or whether it could be improved.Methods: We used healthcare data for 1.4 million specialty mental health and primary care outpatient visits among 256,823 adolescents across 7 health systems. The prediction target was 90-day risk of suicide attempt following a visit. We used logistic regression with least absolute shrinkage and selection operator (LASSO) and generalized estimating equations (GEE) to predict risk. We compared performance of three models: an existing model, a recalibrated version of that model, and a newly-learned model. Models were compared using area under the receiver operating curve (AUC), sensitivity, specificity, positive predictive value and negative predictive value.Results: The AUC produced by the existing model for specialty mental health visits estimated in adolescents alone (0.796; [0.789, 0.802]) was not significantly different than the AUC of the recalibrated existing model (0.794; [0.787, 0.80]) or the newly-learned model (0.795; [0.789, 0.801]). Predicted risk following primary care visits was also similar: existing (0.855; [0.844, 0.866]), recalibrated (0.85 [0.839, 0.862]), newly-learned (0.842, [0.829, 0.854]).Limitations: The models did not incorporate non-healthcare risk factors. The models relied on ICD9-CM codes for diagnoses and outcome measurement.Conclusions: Prediction models already in operational use by health systems can be reliably employed for identifying adolescents in need of further evaluation. [ABSTRACT FROM AUTHOR]

Background: Sharing of research data derived from health system records supports the rigor and reproducibility of primary research and can accelerate research progress through secondary use. But public sharing of such data can create risk of re-identifying individuals, exposing sensitive health information. Method: We describe a framework for assessing re-identification risk that includes: identifying data elements in a research dataset that overlap with external data sources, identifying small classes of records defined by unique combinations of those data elements, and considering the pattern of population overlap between the research dataset and an external source. We also describe alternative strategies for mitigating risk when the external data source can or cannot be directly examined. Results: We illustrate this framework using the example of a large database used to develop and validate models predicting suicidal behavior after an outpatient visit. We identify elements in the research dataset that might create risk and propose a specific risk mitigation strategy: deleting indicators for health system (a proxy for state of residence) and visit year. Discussion: Researchers holding health system data must balance the public health value of data sharing against the duty to protect the privacy of health system members. Specific steps can provide a useful estimate of re-identification risk and point to effective risk mitigation strategies. [ABSTRACT FROM AUTHOR]

Objective: The authors sought to describe patterns of health care use prior to first diagnosis of a psychotic disorder in a population-based sample.Method: Electronic health records and insurance claims from five large integrated health systems were used to identify 624 patients 15-29 years old who received a first diagnosis of a psychotic disorder in any care setting and to record health services received, diagnoses assigned, and medications dispensed during the previous 36 months. Patterns of utilization were compared between patients receiving a first diagnosis of a psychotic disorder and matched samples of general health system members and members receiving a first diagnosis of unipolar depression.Results: During the year before a first psychotic disorder diagnosis, 29% of patients had mental health specialty outpatient care, 8% had mental health inpatient care, 24% had emergency department mental health care, 29% made a primary care visit with a mental health diagnosis, and 60% received at least one mental health diagnosis (including substance use disorders). Compared with patients receiving a first diagnosis of unipolar depression, those with a first diagnosis of a psychotic disorder were modestly more likely to use all types of health services and were specifically more likely to use mental health inpatient care (odds ratio=2.96, 95% CI=1.97-4.43) and mental health emergency department care (rate ratio=3.74, 95% CI=3.39-4.53).Conclusions: Most patients receiving a first diagnosis of a psychotic disorder had some indication of mental health care need during the previous year. General use of primary care or mental health services, however, does not clearly distinguish people who later receive a diagnosis of a psychotic disorder from those who later receive a diagnosis of unipolar depression. Use of inpatient or emergency department mental health care is a more specific indicator of risk. [ABSTRACT FROM AUTHOR]

Background: Behavioral and psychological symptoms of dementia (BPSD) are associated with increased stress, burden, and depression among family caregivers of people with dementia. STAR-Caregivers Virtual Training and Follow-up (STAR-VTF) is adapted from an evidence-based, in-person program that trains family caregivers to manage BPSD. We used a human-centered design approach to obtain feedback from family caregivers about STAR-VTF. The program will be evaluated using a pragmatic randomized trial. Objective: The objective of the study was to understand the needs of family caregivers for improving BPSD management and the extent to which caregivers perceived that STAR-VTF could address those needs. Methods: Between July and September 2019, we conducted 15 semistructured interviews with family caregivers of people with dementia who receive care at Kaiser Permanente Washington in the Seattle metropolitan area. We identified participants from electronic health records, primarily based on a prescription for antipsychotic medication for the person with dementia (a proxy for caregivers dealing with BPSD). We showed caregivers low-fidelity prototypes of STAR-VTF online self-directed materials and verbally described potential design elements. We obtained caregiver feedback on these elements, focusing on their needs and preferences and perceived barriers to using STAR-VTF. We used a hybrid approach of inductive and deductive coding and aggregated codes to develop themes. Results: The idea of a virtual training program for learning to manage BPSD appealed to caregivers. They said health care providers did not provide adequate education in the early disease stages about the personality and behavior symptoms that can affect people with dementia. Caregivers found it unexpected and frustrating when the person with dementia began experiencing BPSD, symptoms they felt unprepared to manage. Accordingly, caregivers expressed a strong desire for the health care organization to offer programs such as STAR-VTF much sooner. Caregivers had already put considerable effort into problem solving challenging behaviors. They anticipated deriving less value from STAR-VTF at that point. Nonetheless, many were interested in the virtual aspect of the training due to the convenience of receiving help from home and the perception that help from a virtual program would be timelier than traditional service modalities (eg, face to face). Given caregivers' limited time, they suggested dividing the STAR-VTF content into chunks to review as time permitted. Caregivers were interested in having a STAR-VTF provider for additional support in managing challenging behaviors. Caregivers reported a preference for having the same coach for the program duration. Conclusions: Caregivers we interviewed would likely accept a virtual training program such as STAR-VTF to obtain information about BPSD and receive help managing it. Family caregivers anticipated deriving more value if STAR-VTF was offered earlier in the disease course. JMIR Aging 2021;4(1):e24965 doi:10.2196/24965 [ABSTRACT FROM AUTHOR]

Rates of child maltreatment (CM) obtained from electronic health records are much lower than national child welfare prevalence rates indicate. There is a need to understand how CM is documented to improve reporting and surveillance. To examine whether using natural language processing (NLP) in outpatient chart notes can identify cases of CM not documented by ICD diagnosis code, the overlap between the coding of child maltreatment by ICD and NLP, and any differences by age, gender, or race/ethnicity. Outpatient chart notes of children age 0–18 years old within Kaiser Permanente Washington (KPWA) 2018–2020 were used to examine a selected set of maltreatment-related terms categorized into concept unique identifiers (CUI). Manual review of text snippets for each CUI was completed to flag for validated cases and retrain the NLP algorithm. The NLP results indicated a crude rate of 1.55 % to 2.36 % (2018–2020) of notes with reference to CM. The rate of CM identified by ICD code was 3.32 per 1000 children, whereas the rate identified by NLP was 37.38 per 1000 children. The groups that increased the most in identification of maltreatment from ICD to NLP were adolescents (13–18 yrs. old), females, Native American children, and those on Medicaid. Of note, all subgroups had substantially higher rates of maltreatment when using NLP. Use of NLP substantially increased the estimated number of children who have been impacted by CM. Accurately capturing this population will improve identification of vulnerable youth at high risk for mental health symptoms. [ABSTRACT FROM AUTHOR]