An interpretable predictive deep learning platform for pediatric metabolic diseases.

OBJECTIVES: Metabolic disease in children is increasing worldwide and predisposes a wide array of chronic comorbid conditions with severe impacts on quality of life. Tools for early detection are needed to promptly intervene to prevent or slow the development of these long-term complications. MATERIALS AND METHODS: No clinically available tools are currently in widespread use that can predict the onset of metabolic diseases in pediatric patients. Here, we use interpretable deep learning, leveraging longitudinal clinical measurements, demographical data, and diagnosis codes from electronic health record data from a large integrated health system to predict the onset of prediabetes, type 2 diabetes (T2D), and metabolic syndrome in pediatric cohorts. RESULTS: The cohort included 49 517 children with overweight or obesity aged 2-18 (54.9% male, 73% Caucasian), with a median follow-up time of 7.5 years and mean body mass index (BMI) percentile of 88.6%. Our model demonstrated area under receiver operating characteristic curve (AUC) accuracies up to 0.87, 0.79, and 0.79 for predicting T2D, metabolic syndrome, and prediabetes, respectively. Whereas most risk calculators use only recently available data, incorporating longitudinal data improved AUCs by 13.04%, 11.48%, and 11.67% for T2D, syndrome, and prediabetes, respectively, versus models using the most recent BMI (P < 2.2 × 10-16). DISCUSSION: Despite most risk calculators using only the most recent data, incorporating longitudinal data improved the model accuracies because utilizing trajectories provides a more comprehensive characterization of the patient's health history. Our interpretable model indicated that BMI trajectories were consistently identified as one of the most influential features for prediction, highlighting the advantages of incorporating longitudinal data when available.

The impact of spiritual care delivered by nurses on patients' comfort: A propensity score matched cohort utilizing electronic health record data.

BACKGROUND: Spiritual care has been associated with better health outcomes. Despite increasing evidence of the benefits of spiritual care for older patients coping with illness and aggressive treatment, the role of spirituality is not well understood and implemented. Nurses, as frontline holistic healthcare providers, are in a position to address patients' spiritual needs and support them in finding meaning in life. This study aimed to identify spiritual care by analyzing nursing data and to compare the psychological and physical comfort between older chronically ill patients who received spiritual care versus those who did not receive spiritual care. MATERIAL AND METHODS: A propensity score matched cohort utilizing nursing care plan data was used to construct balanced groups based on patient characteristics at admission. 45 older patients (≥65 years) with chronic illnesses received spiritual care with measured psychological or physical comfort and 90 matched controls. To ensure the robustness of our results, two sensitivity analyses were performed. Group comparisons were performed to assess the average treatment effect of spiritual care on psychological and physical comfort outcomes. RESULTS: The mean psychological comfort was 4.3 (SD = 0.5) for spiritual care receivers and 3.9 (SD = 0.9) for non-receivers. Regression analysis showed that spiritual care was associated with better psychological comfort (estimate = 0.479, std. error = 0.225, p = 0.041). While its effect on physical comfort was not statistically significant (estimate = -0.265, std. error = 0.234, p = 0.261). This study provides suggestive evidence of the positive impact of nurses' spiritual care in improving psychological comfort for older patients with chronic illnesses. CONCLUSION: Using interoperable nursing data, our findings suggest that spiritual care improves psychological comfort in older patients facing illness. This finding suggests that nurses may integrate spiritual care into their usual care to support patients experiencing distress.

An Electronic Teen Questionnaire, the eTeenQ, for Risk Behavior Screening During Adolescent Well Visits in an Integrated Health System: Development and Pilot Implementation.

Background: Screening for risk behaviors is a routine and essential component of adolescent preventive health visits. Early identification of risks can inform targeted counseling and care. If stored in discrete fields in the electronic health record (EHR), adolescent screening data can also be used to understand risk behaviors across a clinic or health system or to support quality improvement projects. Objective: Goals of this pilot study were to adapt and implement an existing paper adolescent risk behavior screening tool for use as an electronic data capture tool (the eTeenQ), to evaluate acceptance of the eTeenQ, and to describe the prevalence of the selected risk behaviors reported through the eTeenQ. Methods: The multidisciplinary project team applied an iterative process to develop the 29-item eTeenQ. Two unique data entry forms were created with attention to (1) user interface and user experience, (2) the need to maintain patient privacy, and (3) the potential to transmit and store data for future use in clinical care and research. Three primary care clinics within a large health system piloted the eTeenQ from August 17, 2020, to August 27, 2021. During preventive health visits for adolescents aged 12 to 18 years, the eTeenQ was completed on tablets and responses were converted to a provider display for teens and providers to review together. Responses to the eTeenQ were stored in a REDCap (Research Electronic Data Capture; Vanderbilt University) database, and for patients who agreed, responses were transferred to an EHR flowsheet. Responses to selected eTeenQ questions are reported for those consenting to research. At the conclusion of the pilot, the study team conducted semistructured interviews with providers and staff regarding their experience using the eTeenQ. Results: Among 2816 adolescents with well visits, 2098 (74.5%) completed the eTeenQ. Of these, 1811 (86.3%) agreed to store responses in the EHR. Of 1632 adolescents (77.8% of those completing the eTeenQ) who consented for research and remained eligible, 1472 (90.2%) reported having an adult they can really talk to and 1510 (92.5%) reported feeling safe in their community, yet 401 (24.6%) reported someone they lived with had a gun and 172 (10.5%) reported having had a stressful or scary event that still bothered them. In addition, 157 (9.6%) adolescents reported they were or wondered if they were gay, lesbian, bisexual, pansexual, asexual, or other, and 43 (2.6%) reported they were or wondered if they were transgender or gender diverse. Of 11 staff and 7 providers completing interviews, all felt that the eTeenQ improved confidentiality and willingness among adolescents to answer sensitive questions. All 7 providers preferred the eTeenQ over the paper screening tool. Conclusions: Electronic capture of adolescent risk behaviors is feasible in a busy clinic setting and well accepted among staff and clinicians. Most adolescents agreed for their responses to risk behavior screening to be stored in the EHR.

Effectiveness of Medical Music Therapy Practice: Integrative Research Using the Electronic Health Record: Rationale, Design, and Population Characteristics.

Background: Several clinical trials support the efficacy of music therapy (MT) for improving outcomes in hospitalized patients, but few studies have evaluated the real-world delivery and integration of MT across multiple medical centers. This article describes the rationale, design, and population characteristics of a retrospective study examining the delivery and integration of MT within a large health system. Methods: A retrospective electronic health record (EHR) review was conducted of hospitalized patients seen by and/or referred to MT between January 2017 and July 2020. MT was provided across ten medical centers, including an academic medical center, a freestanding cancer center, and eight community hospitals. Discrete demographic, clinical, and MT treatment and referral characteristics were extracted from the EHR, cleaned, and organized using regular expressions functions, and they were summarized using descriptive statistics. Results: The MT team (average 11.6 clinical fulltime equivalent staff/year) provided 14,261 sessions to 7378 patients across 9091 hospitalizations. Patients were predominantly female (63.7%), White (54.3%) or Black/African American (44.0%), 63.7 ± 18.5 years of age at admission, and insured under Medicare (51.1%), Medicaid (18.1%), or private insurance (14.2%). Patients' hospitalizations (median length of stay: 5 days) were primarily for cardiovascular (11.8%), respiratory (9.9%), or musculoskeletal (8.9%) conditions. Overall, 39.4% of patients' hospital admissions included a mental health diagnosis, and 15.4% were referred to palliative care. Patients were referred by physicians (34.7%), nurses (29.4%), or advanced practice providers (24.7%) for coping (32.0%), anxiety reduction (20.4%), or pain management (10.1%). Therapists provided sessions to patients discharged from medical/surgical (74.5%), oncology (18.4%), or intensive care (5.8%) units. Conclusions: This retrospective study indicates that MT can be integrated across a large health system for addressing the needs of socioeconomically diverse patients. However, future research is needed to assess MT's impact on health care utilization (i.e., length of stay and rates of readmission) and immediate patient-reported outcomes.

Identification of recurrent atrial fibrillation using natural language processing applied to electronic health records.

AIMS: This study aimed to develop and apply natural language processing (NLP) algorithms to identify recurrent atrial fibrillation (AF) episodes following rhythm control therapy initiation using electronic health records (EHRs). METHODS AND RESULTS: We included adults with new-onset AF who initiated rhythm control therapies (ablation, cardioversion, or antiarrhythmic medication) within two US integrated healthcare delivery systems. A code-based algorithm identified potential AF recurrence using diagnosis and procedure codes. An automated NLP algorithm was developed and validated to capture AF recurrence from electrocardiograms, cardiac monitor reports, and clinical notes. Compared with the reference standard cases confirmed by physicians' adjudication, the F-scores, sensitivity, and specificity were all above 0.90 for the NLP algorithms at both sites. We applied the NLP and code-based algorithms to patients with incident AF (n = 22 970) during the 12 months after initiating rhythm control therapy. Applying the NLP algorithms, the percentages of patients with AF recurrence for sites 1 and 2 were 60.7% and 69.9% (ablation), 64.5% and 73.7% (cardioversion), and 49.6% and 55.5% (antiarrhythmic medication), respectively. In comparison, the percentages of patients with code-identified AF recurrence for sites 1 and 2 were 20.2% and 23.7% for ablation, 25.6% and 28.4% for cardioversion, and 20.0% and 27.5% for antiarrhythmic medication, respectively. CONCLUSION: When compared with a code-based approach alone, this study's high-performing automated NLP method identified significantly more patients with recurrent AF. The NLP algorithms could enable efficient evaluation of treatment effectiveness of AF therapies in large populations and help develop tailored interventions.

Nonsteroidal anti-inflammatory drug "allergy" labeling is associated with increased postpartum opioid utilization.

BACKGROUND: Current guidelines recommend a stepwise approach to postpartum pain management, beginning with acetaminophen and nonsteroidal anti-inflammatory drugs (NSAIDs), with opioids added only if needed. Report of a prior NSAID-induced adverse drug reaction (ADR) may preclude use of first-line analgesics, despite evidence that many patients with this allergy label may safely tolerate NSAIDs. OBJECTIVE: We assessed the association between reported NSAID ADRs and postpartum opioid utilization. METHODS: We performed a retrospective cohort study of birthing people who delivered within an integrated health system (January 1, 2017, to December 31, 2020). Study outcomes were postpartum inpatient opioid administrations and opioid prescriptions at discharge. Statistical analysis was performed on a propensity score-matched sample, which was generated with the goal of matching to the covariate distributions from individuals with NSAID ADRs. RESULTS: Of 38,927 eligible participants, there were 883 (2.3%) with an NSAID ADR. Among individuals with reported NSAID ADRs, 49.5% received inpatient opioids in the postpartum period, compared to 34.5% of those with no NSAID ADRs (difference = 15.0%, 95% confidence interval 11.4-18.6%). For patients who received postpartum inpatient opioids, those with NSAID ADRs received a higher total cumulative dose between delivery and hospital discharge (median 30.0 vs 22.5 morphine milligram equivalents [MME] for vaginal deliveries; median 104.4 vs 75.0 MME for cesarean deliveries). The overall proportion of patients receiving an opioid prescription at the time of hospital discharge was higher for patients with NSAID ADRs compared to patients with no NSAID ADRs (39.3% vs 27.2%; difference = 12.1%, 95% confidence interval 8.6-15.6%). CONCLUSION: Patients with reported NSAID ADRs had higher postpartum inpatient opioid utilization and more frequently received opioid prescriptions at hospital discharge compared to those without NSAID ADRs, regardless of mode of delivery.

Adoption of Electronic Health Record Among Substance Use Disorder Treatment Programs: Nationwide Cross-Sectional Survey Study.

BACKGROUND: Electronic health record (EHR) systems have been shown to be associated with improvements in care processes, quality of care, and patient outcomes. EHR also has a crucial role in the delivery of substance use disorder (SUD) treatment and is considered important for addressing SUD crises, including the opioid epidemic. However, little is known about the adoption of EHR in SUD treatment programs or the organizational-level factors associated with the adoption of EHR in SUD treatment. OBJECTIVE: We examined the adoption of EHR in SUD programs, with a focus on changes in adoption from 2014 to 2017, and identified organizational-level factors associated with EHR adoption. METHODS: We used data from the 2014 and 2017 National Drug Abuse Treatment System Surveys. Our analysis included 1027 SUD programs (531 in 2014 and 496 in 2017). We used chi-square and Mann-Whitney U tests for categorical and continuous variables, respectively, to assess changes in EHR adoption, technology use, program, and client characteristics. We also investigated differences in characteristics and barriers to adoption by EHR adoption status (adopted EHR vs had not adopted or were planning to adopt EHR). We then conducted multivariate logistic regressions to examine internal and external factors associated with EHR adoption. RESULTS: The adoption of EHR increased significantly from 57.6% (306/531) in 2014 to 69.2% (343/496) in 2017 (P<.001), showing that nearly one-third (153/496, 30.8%) of SUD programs had not yet adopted an EHR system by 2017. We identified a significant increase in technology use and ownership by a parent company (P=.01 and P<.001) and a decrease in the percentage of uninsured patients in 2017 (P<.001), compared to 2014. Our analysis further showed significant differences by adoption status for three major barriers to adoption: (1) start-up costs, (2) ongoing financial costs, and (3) privacy or security concerns (P<.001). Programs that used computerized scheduling (adjusted odds ratio [AOR] 3.02, 95% CI 2.23-4.09) and billing systems (AOR 2.29, 95% CI 1.62-3.25) were more likely to adopt EHR. Similarly, ownership type, such as private nonprofit (AOR 1.86, 95% CI 1.31-2.65) and public (AOR 2.14, 95% CI 1.27-3.67), or interest in participating in a patient-centered medical home (AOR 1.93, 95% CI 1.29-2.92), were associated with an increased likelihood to adopt EHR. Overall, SUD programs were more likely to adopt an EHR system in 2017 compared to 2014 (AOR 1.44, 95% CI 1.07-1.94). CONCLUSIONS: Our findings highlighted that SUD programs may be on track to achieve widespread EHR adoption. However, there is a need for focused strategies, resources, and policies explicitly designed to systematically address barriers and tackle obstacles to expanding the adoption of EHR systems. These efforts must be holistic and address factors at multiple organizational levels.

Protocol to evaluate sequential electronic health record-based strategies to increase genetic testing for breast and ovarian cancer risk across diverse patient populations in gynecology practices.

BACKGROUND: Germline genetic testing is recommended by the National Comprehensive Cancer Network (NCCN) for individuals including, but not limited to, those with a personal history of ovarian cancer, young-onset (< 50 years) breast cancer, and a family history of ovarian cancer or male breast cancer. Genetic testing is underused overall, and rates are consistently lower among Black and Hispanic populations. Behavioral economics-informed implementation strategies, or nudges, directed towards patients and clinicians may increase the use of this evidence-based clinical practice. METHODS: Patients meeting eligibility for germline genetic testing for breast and ovarian cancer will be identified using electronic phenotyping algorithms. A pragmatic cohort study will test three sequential strategies to promote genetic testing, two directed at patients and one directed at clinicians, deployed in the electronic health record (EHR) for patients in OB-GYN clinics across a diverse academic medical center. We will use rapid cycle approaches informed by relevant clinician and patient experiences, health equity, and behavioral economics to optimize and de-risk our strategies and methods before trial initiation. Step 1 will send patients messages through the health system patient portal. For non-responders, step 2 will reach out to patients via text message. For non-responders, Step 3 will contact patients' clinicians using a novel "pend and send" tool in the EHR. The primary implementation outcome is engagement with germline genetic testing for breast and ovarian cancer predisposition, defined as a scheduled genetic counseling appointment. Patient data collected through the EHR (e.g., race/ethnicity, geocoded address) will be examined as moderators of the impact of the strategies. DISCUSSION: This study will be one of the first to sequentially examine the effects of patient- and clinician-directed strategies informed by behavioral economics on engagement with breast and ovarian cancer genetic testing. The pragmatic and sequential design will facilitate a large and diverse patient sample, allow for the assessment of incremental gains from different implementation strategies, and permit the assessment of moderators of strategy effectiveness. The findings may help determine the impact of low-cost, highly transportable implementation strategies that can be integrated into healthcare systems to improve the use of genomic medicine. TRIAL REGISTRATION: ClinicalTrials.gov. NCT05721326. Registered February 10, 2023. https://www. CLINICALTRIALS: gov/study/NCT05721326.

Using natural language processing to identify the status of homelessness and housing instability among serious illness patients from clinical notes in an integrated healthcare system.

Background: Efficiently identifying the social risks of patients with serious illnesses (SIs) is the critical first step in providing patient-centered and value-driven care for this medically vulnerable population. Objective: To apply and further hone an existing natural language process (NLP) algorithm that identifies patients who are homeless/at risk of homeless to a SI population. Methods: Patients diagnosed with SI between 2019 and 2020 were identified using an adapted list of diagnosis codes from the Center for Advance Palliative Care from the Kaiser Permanente Southern California electronic health record. Clinical notes associated with medical encounters within 6 months before and after the diagnosis date were processed by a previously developed NLP algorithm to identify patients who were homeless/at risk of homelessness. To improve the generalizability to the SI population, the algorithm was refined by multiple iterations of chart review and adjudication. The updated algorithm was then applied to the SI population. Results: Among 206 993 patients with a SI diagnosis, 1737 (0.84%) were identified as homeless/at risk of homelessness. These patients were more likely to be male (51.1%), age among 45-64 years (44.7%), and have one or more emergency visit (65.8%) within a year of their diagnosis date. Validation of the updated algorithm yielded a sensitivity of 100.0% and a positive predictive value of 93.8%. Conclusions: The improved NLP algorithm effectively identified patients with SI who were homeless/at risk of homelessness and can be used to target interventions for this vulnerable group.

Effectiveness of music therapy within community hospitals: an EMMPIRE retrospective study.

Introduction: Given the challenges health systems face in providing effective nonpharmacologic treatment for pain and psychological distress, clinical effectiveness studies of evidence-based strategies such as music therapy (MT) are needed. Objectives: This study examined changes in patient-reported outcomes (PROs) after MT and explored variables associated with pain reduction of ≥2 units on a 0 to 10 numeric rating scale (NRS). Methods: A retrospective review was conducted on initial MT interventions provided to adults receiving community hospital care between January 2017 and July 2020. Sessions were included if participants reported pre-session pain, anxiety, and/or stress scores of ≥4 on the NRS. Data analysis included a bootstrap analysis of single-session changes in PROs and a logistic regression exploring variables associated with pain reduction (ie, ≥2 units vs <2 units). Results: Patients (n = 1056; mean age: 63.83 years; 76.1% female; 57.1% White; 41.1% Black/African American) reported clinically significant mean reductions in pain (2.04 units), anxiety (2.80 units), and stress (3.48 units). After adjusting for demographic, clinical, and operational characteristics in the model (c-statistic = 0.668), patients receiving an MT session in which pain management was a goal were 4.32 times more likely (95% confidence interval 2.26, 8.66) to report pain reduction of ≥2 units than patients receiving an MT session in which pain management was not a session goal. Conclusion: This retrospective study supports the clinical effectiveness of MT for symptom management in community hospitals. However, additional research is needed to determine which characteristics of MT interventions and patients influence pain change.

Building a haemophilia-specific navigator in Epic® Electronic Health Records: Single-centre preliminary experience.

INTRODUCTION: With the increasing complexity of haemophilia care and the advent of numerous therapeutic innovations, there is an unmet need for documentation and data collection tools tailored to people with haemophilia (PwH). To date, no fully integrated haemophilia-specific electronic health record (EHR) has been described in the literature. AIM: To evaluate the feasibility of integrating a haemophilia-specific navigator into the Epic EHR. METHODS: Based on clinical experience and registry datasets, we identified key variables describing both PwH and carriers of haemophilia. These were then incorporated into a REDCap database, which served as a starting point for the development of a comprehensive haemophilia flowsheet. We built a dedicated haemophilia navigator within Epic that includes a flowsheet featuring up to 212 variables, as well as customised note templates and patient lists integrating data from the haemophilia flowsheet. RESULTS: It was feasible to develop a haemophilia navigator within Epic over the course of 12 months. The navigator's flowsheet enables systematic and comprehensive clinical assessment of PwH and carriers, while customised patient lists provide a quick summary of each patient's profile to the haemophilia treatment centre staff and highlight issues that require an intervention. In our clinical practice, patients actively participated in the new documentation process and responded positively to the navigator. CONCLUSION: Adapting EHRs to the needs of PwH and carriers promotes holistic care for this population and provides an opportunity for patient empowerment. Such haemophilia-specific EHRs are expected to promote standardisation of care and facilitate the collection of registry data on a national and international level.

Extracting Complementary and Integrative Health Approaches in Electronic Health Records.

Complementary and Integrative Health (CIH) has gained increasing popularity in the past decades. While the evidence bases to support them are growing, there is still a gap in understanding their effects and potential adverse events using real-world data. The overall goal of this study is to represent information pertinent to both psychological and physical CIH approaches (specifically, using examples of music therapy, chiropractic, and aquatic exercise in this study) in an electronic health record (EHR) system. We also aim to evaluate the ability of existing natural language processing (NLP) systems to identify CIH approaches. A total of 300 notes were randomly selected and manually annotated. Annotations were made for status, symptom, and frequency of each approach. This set of annotations was used as a gold standard to evaluate the performance of NLP systems used in this study (specifically BioMedICUS, MetaMap, and cTAKES) for extracting CIH concepts. Venn diagram was used to investigate the consistency of medical records searching by Current Procedural Terminology (CPT) codes and CIH approaches keywords in SQL. Since CPT codes usually do not have specific mentions of CIH approaches, the Venn diagram had less overlap with those found in clinical notes for all three CIH therapies. The three NLP systems achieved 0.41 in average lenient match F1-score in all three CIH approaches, respectively. BioMedICUS achieved the best performance in aquatic exercise with an F1-score of 0.66. This study contributes to the overall representation of CIH in clinical note and lays a foundation for using EHR for clinical research for CIH approaches.

A quality improvement initiative to improve folic acid supplementation counseling for adolescent females with epilepsy.

OBJECTIVE: We designed a quality improvement (QI) project to improve rates of documented folic acid supplementation counseling for adolescent females with epilepsy, consistent with a quality measure from the American Academy of Neurology and American Epilepsy Society. Our SMART aim was to increase the percentage of visits at which folic acid counseling was addressed from our baseline rate of 23% to 50% by July 1, 2020. METHODS: This initiative was conducted in female patients ≥12 years old with epilepsy who were prescribed daily antiseizure medication and were seen by the 13 providers in our Neurology QI Program. Using provider interviews, we undertook a root cause analysis of low counseling rates and identified the following main factors: insufficient time during clinic visit to counsel, lack of provider knowledge, and forgetting to counsel. Countermeasures were designed to address these main root causes and were implemented through iterative plan-do-study-act (PDSA) cycles. Interventions included provider education and features within the electronic health record, which were introduced sequentially, culminating in the creation of a best practice advisory (BPA). We performed biweekly chart reviews of visits for applicable patients to establish baseline performance rate and track progress over time. We used a statistical process control p-chart to analyze the outcome measure of documented counseling. As a balancing measure, clinicians were surveyed using the Technology Adoption Model survey to assess acceptance of the BPA. RESULTS: From September 2019 to August 2022, the QI team improved rates of documented folic acid counseling from 23% to 73% through several PDSA cycles. This level of performance has been sustained over time. The most successful and sustainable intervention was the BPA. Provider acceptance of the BPA was overall positive. SIGNIFICANCE: We successfully used QI methodology to improve and sustain our rates of documented folic acid supplementation counseling for adolescent females with epilepsy.

Optimizing Patient-Reported Outcome Collection and Documentation in Medical Music Therapy: Process-Improvement Study.

BACKGROUND: To measure the effectiveness of nonpharmacologic interventions delivered during clinical care, investigators need to ensure robust and routine data collection without disrupting individualized patient care or adding unnecessary documentation burden. OBJECTIVE: A process-improvement study was undertaken to improve documentation consistency and increase the capture of patient-reported outcomes (PROs; ie, stress, pain, anxiety, and coping) within a medical music therapy (MT) team. METHODS: We used 2 Plan-Do-Study-Act (PDSA) cycles to improve documentation processes among an MT team (13.3 clinical full-time equivalent staff). Trainings focused on providing skills and resources for optimizing pre- and postsession PRO collection, specific guidelines for entering session data in the electronic health record, and opportunities for the team to provide feedback. Two comparisons of therapists' PRO collection rates were conducted: (1) between the 6 months before PDSA Cycle 1 (T0) and PDSA Cycle 1 (T1), and (2) between T1 and PDSA Cycle 2 (T2). RESULTS: Music therapists' rates of capturing any PRO within MT sessions increased significantly (P<.001) from T0 to T1 and from T1 to T2 for all domains, including stress (4/2758, 0.1% at T0; 1012/2786, 36.3% at T1; and 393/775, 50.7% at T2), pain (820/2758, 29.7% at T0; 1444/2786, 51.8% at T1; and 476/775, 61.4% at T2), anxiety (499/2758, 18.1% at T0; 950/2786, 34.1% at T1; and 400/775, 51.6% at T2), and coping (0/2758, 0% at T0; 571/2786, 20.5% at T1; and 319/775, 41.2% at T2). Music therapists' feedback and findings from a retrospective analysis were used to create an improved electronic health record documentation template. CONCLUSIONS: Rates of PRO data collection improved within the medical MT team. Although the process improvement in this study was applied to a nonpharmacologic MT intervention, the principles are applicable to numerous inpatient clinical providers. As hospitals continue to implement nonpharmacologic therapies in response to the Joint Commission's recommendations, routine PRO collection will provide future researchers with the ability to evaluate the impact of these therapies on pain relief and opioid use.

Use of comprehensive recruitment strategies in the glycemia reduction approaches in diabetes: A comparative effectiveness study (GRADE) multi-center clinical trial.

BACKGROUND/AIMS: We present and describe recruitment strategies implemented from 2013 to 2017 across 45 clinical sites in the United States, participating in the Glycemia Reduction Approaches in Diabetes: A Comparative Effectiveness Study, an unmasked, randomized controlled trial evaluating four glucose-lowering medications added to metformin in individuals with type 2 diabetes mellitus (duration of diabetes <10 years). We examined the yield of participants recruited through Electronic Health Records systems compared to traditional recruitment methods to leverage access to type 2 diabetes patients in primary care. METHODS: Site selection criteria included availability of the study population, geographic representation, the ability to recruit and retain a diverse pool of participants including traditionally underrepresented groups, and prior site research experience in diabetes clinical trials. Recruitment initiatives were employed to support and monitor recruitment, such as creation of a Recruitment and Retention Committee, development of criteria for Electronic Health Record systems queries, conduct of remote site visits, development of a public screening website, and other central and local initiatives. Notably, the study supported a dedicated recruitment coordinator at each site to manage local recruitment and facilitate screening of potential participants identified by Electronic Health Record systems. RESULTS: The study achieved the enrollment goal of 5000 participants, meeting its target with Black/African American (20%), Hispanic/Latino (18%), and age ≧60 years (42%) subgroups but not with women (36%). Recruitment required 1 year more than the 3 years originally planned. Sites included academic hospitals, integrated health systems, and Veterans Affairs Medical Centers. Participants were enrolled through Electronic Health Record queries (68%), physician referral (13%), traditional mail outreach (7%), TV, radio, flyers, and Internet (7%), and other strategies (5%). Early implementation of targeted Electronic Health Record queries yielded a greater number of eligible participants compared to other recruitment methods. Efforts over time increasingly emphasized engagement with primary care networks. CONCLUSION: Glycemia Reduction Approaches in Diabetes: A Comparative Effectiveness successfully recruited a diverse study population with relatively new onset of type 2 diabetes mellitus, relying to a large extent on the use of Electronic Health Record to screen potential participants. A comprehensive approach to recruitment with frequent monitoring was critical to meet the recruitment goal.

Woman-centred care and integrated electronic medical records within Australian maternity settings: Point prevalence audit and observational study.

OBJECTIVE: Transition to paperless records brings new challenges to midwifery practice across the continuum of woman-centred care. There is limited and conflicting evidence on the relative benefits of electronic medical records in maternity settings. This article aims to inform the use of integrative electronic medical records within the maternity services' environment with attention to the midwife-woman relationship. DESIGN: This descriptive two-part study includes 1) an audit of electronic records in the early period following implementation (2-time points); and 2) an observational study to observe midwives' practice relating to electronic record use. SETTING: Two regional tertiary public hospitals PARTICIPANTS: Midwives providing care for childbearing women across antenatal, intrapartum and postnatal areas. FINDINGS: 400 integrated electronic medical records were audited for completeness. Most fields had high levels of complete data in the correct location. However, between time 1 (T1) and time 2 (T2), persistent missing data (foetal heart rate documented 30 minutely T1 36%; T2 42%), and incomplete or incorrectly located data (pathology results T1:63%; T2 54%; perineal repair T1 60%; T2 46%) were identified. Observationally, midwives were actively engaged with the integrative electronic medical record between 23% to 68% (median 46%; IQR 16) of the time. CONCLUSION: Midwives spent a significant amount of time completing documentation during clinical episodes of care. Largely, this documentation was found to be accurate, yet exceptions to data completeness, precision and location remained, indicating some concerns with software usability. IMPLICATIONS FOR PRACTICE: Time-intensive monitoring and documentation may hinder woman-centred midwifery care.

Usability Testing of a New Digital Integrated Health Ecosystem (PainRELife) for the Clinical Management of Chronic Pain in Patients With Early Breast Cancer: Protocol for a Pilot Study.

BACKGROUND: Chronic pain (CP) and its management are critical issues in the care pathway of patients with breast cancer. Considering the complexity of CP experience in cancer, the international scientific community has advocated identifying cutting-edge approaches for CP management. Recent advances in the field of health technology enable the adoption of a novel approach to care management by developing integrated ecosystems and mobile health apps. OBJECTIVE: The primary end point of this pilot study is to evaluate patients' usability experience at 3 months of a new digital and integrated technological ecosystem, PainRELife, for CP in a sample of patients with breast cancer. The PainRELife ecosystem is composed of 3 main technological assets integrated into a single digital ecosystem: Fast Healthcare Interoperability Resources-based cloud platform (Nu platform) that enables care pathway definition and data collection; a big data infrastructure connected to the Fast Healthcare Interoperability Resources server that analyzes data and implements dynamic dashboards for aggregate data visualization; and an ecosystem of personalized applications for patient-reported outcomes collection, digital delivery of interventions and tailored information, and decision support of patients and caregivers (PainRELife app). METHODS: This is an observational, prospective pilot study. Twenty patients with early breast cancer and chronic pain will be enrolled at the European Institute of Oncology at the Division of Medical Senology and the Division of Pain Therapy and Palliative Care. Each patient will use the PainRELife mobile app for 3 months, during which data extracted from the questionnaires will be sent to the Nu Platform that health care professionals will manage. This pilot study is nested in a large-scale project named "PainRELife," which aims to develop a cloud technology platform to interoperate with institutional systems and patients' devices to collect integrated health care data. The study received approval from the Ethical Committee of the European Cancer Institute in December 2021 (number R1597/21-IEO 1701). RESULTS: The recruitment process started in May 2022 and ended in October 2022. CONCLUSIONS: The new integrated technological ecosystems might be considered an encouraging affordance to enhance a patient-centered approach to managing patients with cancer. This pilot study will inform about which features the health technological ecosystems should have to be used by cancer patients to manage CP. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/41216.

Associations of clinical personnel characteristics and controlled substance prescribing practices.

CONTEXT: Over 68,000 deaths were attributed to opioid-related overdose in 2020. Evaluative studies have shown that states that utilized Prescription Drug Monitoring Program (PDMP) systems have decreased opioid-related deaths. With the growing use of PDMPs and an ongoing opioid epidemic, determining the demographics of physicians at risk of overprescribing can elucidate prescribing practices and inform recommendations to change prescribing behaviors. OBJECTIVES: This study aims to assess prescribing behaviors by physicians in 2021 based on four demographics utilizing the National Electronic Health Record System (NEHRS): physician's age, sex, specialty, and degree (MD or Doctor of Osteopathic Medicine [DO]). METHODS: We performed a cross-sectional study of the 2021 NEHRS to determine the relationship between physician characteristics and PDMP use on opioid-prescribing behaviors. Differences between groups were measured via design-based chi-square tests. We constructed multivariable logistic regression models to assess the relationships, via adjusted odds ratios (AOR), between physician characteristics and alternate prescribing patterns. RESULTS: Compared to female physicians, male physicians were more likely to alter their original prescription to reduce morphine milligram equivalents (MMWs) prescribed for a patient (AOR: 1.60; CI: 1.06-2.39; p=0.02), to change to a nonopioid/nonpharmacologic option (AOR: 1.91; 95 % CI: 1.28-2.86; p=0.002), to prescribe naloxone (AOR=2.06; p=0.039), or to refer for additional treatment (AOR=2.07; CI: 1.36-3.16; p<0.001). Compared to younger physicians, those over the age of 50 were less likely to change their prescription to a nonopioid/nonpharmacologic option (AOR=0.63; CI: 0.44-0.90; p=0.01) or prescribe naloxone (AOR=0.56, CI: 0.33-0.92; p=0.02). CONCLUSIONS: Our results showed a statistically significant difference between specialty category and frequency of prescribing controlled substances. After checking the PDMP, male physicians were more likely to alter their original prescription to include harm-reduction strategies. Optimizing the use of PDMP systems may serve to improve prescribing among US physicians.

Digital Health Data Quality Issues: Systematic Review.

BACKGROUND: The promise of digital health is principally dependent on the ability to electronically capture data that can be analyzed to improve decision-making. However, the ability to effectively harness data has proven elusive, largely because of the quality of the data captured. Despite the importance of data quality (DQ), an agreed-upon DQ taxonomy evades literature. When consolidated frameworks are developed, the dimensions are often fragmented, without consideration of the interrelationships among the dimensions or their resultant impact. OBJECTIVE: The aim of this study was to develop a consolidated digital health DQ dimension and outcome (DQ-DO) framework to provide insights into 3 research questions: What are the dimensions of digital health DQ? How are the dimensions of digital health DQ related? and What are the impacts of digital health DQ? METHODS: Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, a developmental systematic literature review was conducted of peer-reviewed literature focusing on digital health DQ in predominately hospital settings. A total of 227 relevant articles were retrieved and inductively analyzed to identify digital health DQ dimensions and outcomes. The inductive analysis was performed through open coding, constant comparison, and card sorting with subject matter experts to identify digital health DQ dimensions and digital health DQ outcomes. Subsequently, a computer-assisted analysis was performed and verified by DQ experts to identify the interrelationships among the DQ dimensions and relationships between DQ dimensions and outcomes. The analysis resulted in the development of the DQ-DO framework. RESULTS: The digital health DQ-DO framework consists of 6 dimensions of DQ, namely accessibility, accuracy, completeness, consistency, contextual validity, and currency; interrelationships among the dimensions of digital health DQ, with consistency being the most influential dimension impacting all other digital health DQ dimensions; 5 digital health DQ outcomes, namely clinical, clinician, research-related, business process, and organizational outcomes; and relationships between the digital health DQ dimensions and DQ outcomes, with the consistency and accessibility dimensions impacting all DQ outcomes. CONCLUSIONS: The DQ-DO framework developed in this study demonstrates the complexity of digital health DQ and the necessity for reducing digital health DQ issues. The framework further provides health care executives with holistic insights into DQ issues and resultant outcomes, which can help them prioritize which DQ-related problems to tackle first.