Evaluation of a Primary Care-Integrated Mobile Health Intervention to Monitor between-Visit Asthma Symptoms.

OBJECTIVES: This study aimed to evaluate implementation of a digital remote symptom monitoring intervention that delivered weekly symptom questionnaires and included the option to receive nurse callbacks via a mobile app for asthma patients in primary care. METHODS: Research questions were structured by the NASSS (Nonadoption, Abandonment, Scale-up Spread, and Sustainability) framework. Quantitative and qualitative methods assessed scalability of the electronic health record (EHR)-integrated app intervention implemented in a 12-month randomized controlled trial. Data sources included patient asthma control questionnaires; app usage logs; EHRs; and interviews and discussions with patients, primary care providers (PCPs), and nurses. RESULTS: We included app usage data from 190 patients and interview data from 21 patients and several clinician participants. Among 190 patients, average questionnaire completion rate was 72.3% and retention was 78.9% (i.e., 150 patients continued to use the app at the end of the trial period). App use was lower among Hispanic and younger patients and those with fewer years of education. Of 1,185 nurse callback requests offered to patients. Thirty-three (2.8%) were requested. Of 84 PCP participants, 14 (16.7%) accessed the patient-reported data in the EHR. Analyses showed that the intervention was appropriate for all levels of asthma control; had no major technical barriers; was desirable and useful for patient treatment; involved achievable tasks for patients; required modest role changes for clinicians; and was a minimal burden on the organization. CONCLUSION: A clinically integrated symptom monitoring intervention has strong potential for sustained adoption. Inequitable adoption remains a concern. PCP use of patient-reported data during visits could improve intervention adoption but may not be required for patient benefits.

Adverse diagnostic events in hospitalised patients: a single-centre, retrospective cohort study.

BACKGROUND: Adverse event surveillance approaches underestimate the prevalence of harmful diagnostic errors (DEs) related to hospital care. METHODS: We conducted a single-centre, retrospective cohort study of a stratified sample of patients hospitalised on general medicine using four criteria: transfer to intensive care unit (ICU), death within 90 days, complex clinical events, and none of the aforementioned high-risk criteria. Cases in higher-risk subgroups were over-sampled in predefined percentages. Each case was reviewed by two adjudicators trained to judge the likelihood of DE using the Safer Dx instrument; characterise harm, preventability and severity; and identify associated process failures using the Diagnostic Error Evaluation and Research Taxonomy modified for acute care. Cases with discrepancies or uncertainty about DE or impact were reviewed by an expert panel. We used descriptive statistics to report population estimates of harmful, preventable and severely harmful DEs by demographic variables based on the weighted sample, and characteristics of harmful DEs. Multivariable models were used to adjust association of process failures with harmful DEs. RESULTS: Of 9147 eligible cases, 675 were randomly sampled within each subgroup: 100% of ICU transfers, 38.5% of deaths within 90 days, 7% of cases with complex clinical events and 2.4% of cases without high-risk criteria. Based on the weighted sample, the population estimates of harmful, preventable and severely harmful DEs were 7.2% (95% CI 4.66 to 9.80), 6.1% (95% CI 3.79 to 8.50) and 1.1% (95% CI 0.55 to 1.68), respectively. Harmful DEs were frequently characterised as delays (61.9%). Severely harmful DEs were frequent in high-risk cases (55.1%). In multivariable models, process failures in assessment, diagnostic testing, subspecialty consultation, patient experience, and history were significantly associated with harmful DEs. CONCLUSIONS: We estimate that a harmful DE occurred in 1 of every 14 patients hospitalised on general medicine, the majority of which were preventable. Our findings underscore the need for novel approaches for adverse DE surveillance.

Patient-Clinician Diagnostic Concordance upon Hospital Admission.

OBJECTIVES: This study aimed to pilot an application-based patient diagnostic questionnaire (PDQ) and assess the concordance of the admission diagnosis reported by the patient and entered by the clinician. METHODS: Eligible patients completed the PDQ assessing patients' understanding of and confidence in the diagnosis 24 hours into hospitalization either independently or with assistance. Demographic data, the hospital principal problem upon admission, and International Classification of Diseases 10th Revision (ICD-10) codes were retrieved from the electronic health record (EHR). Two physicians independently rated concordance between patient-reported diagnosis and clinician-entered principal problem as full, partial, or no. Discrepancies were resolved by consensus. Descriptive statistics were used to report demographics for concordant (full) and nonconcordant (partial or no) outcome groups. Multivariable logistic regressions of PDQ questions and a priori selected EHR data as independent variables were conducted to predict nonconcordance. RESULTS: A total of 157 (77.7%) questionnaires were completed by 202 participants; 77 (49.0%), 46 (29.3%), and 34 (21.7%) were rated fully concordant, partially concordant, and not concordant, respectively. Cohen's kappa for agreement on preconsensus ratings by independent reviewers was 0.81 (0.74, 0.88). In multivariable analyses, patient-reported lack of confidence and undifferentiated symptoms (ICD-10 "R-code") for the principal problem were significantly associated with nonconcordance (partial or no concordance ratings) after adjusting for other PDQ questions (3.43 [1.30, 10.39], p = 0.02) and in a model using selected variables (4.02 [1.80, 9.55], p < 0.01), respectively. CONCLUSION: About one-half of patient-reported diagnoses were concordant with the clinician-entered diagnosis on admission. An ICD-10 "R-code" entered as the principal problem and patient-reported lack of confidence may predict patient-clinician nonconcordance early during hospitalization via this approach.

Effect of digital tools to promote hospital quality and safety on adverse events after discharge.

OBJECTIVES: Post-discharge adverse events (AEs) are common and heralded by new and worsening symptoms (NWS). We evaluated the effect of electronic health record (EHR)-integrated digital tools designed to promote quality and safety in hospitalized patients on NWS and AEs after discharge. MATERIALS AND METHODS: Adult general medicine patients at a community hospital were enrolled. We implemented a dashboard which clinicians used to assess safety risks during interdisciplinary rounds. Post-implementation patients were randomized to complete a discharge checklist whose responses were incorporated into the dashboard. Outcomes were assessed using EHR review and 30-day call data adjudicated by 2 clinicians and analyzed using Poisson regression. We conducted comparisons of each exposure on post-discharge outcomes and used selected variables and NWS as independent predictors to model post-discharge AEs using multivariable logistic regression. RESULTS: A total of 260 patients (122 pre, 71 post [dashboard], 67 post [dashboard plus discharge checklist]) enrolled. The adjusted incidence rate ratios (aIRR) for NWS and AEs were unchanged in the post- compared to pre-implementation period. For patient-reported NWS, aIRR was non-significantly higher for dashboard plus discharge checklist compared to dashboard participants (1.23 [0.97,1.56], P = .08). For post-implementation patients with an AE, aIRR for duration of injury (>1 week) was significantly lower for dashboard plus discharge checklist compared to dashboard participants (0 [0,0.53], P < .01). In multivariable models, certain patient-reported NWS were associated with AEs (3.76 [1.89,7.82], P < .01). DISCUSSION: While significant reductions in post-discharge AEs were not observed, checklist participants experiencing a post-discharge AE were more likely to report NWS and had a shorter duration of injury. CONCLUSION: Interventions designed to prompt patients to report NWS may facilitate earlier detection of AEs after discharge. CLINICALTRIALS.GOV: NCT05232656.

Assessing Equitable Recruitment in a Digital Health Trial for Asthma.

OBJECTIVE: This study aimed to assess a multipronged strategy using primarily digital methods to equitably recruit asthma patients into a clinical trial of a digital health intervention. METHODS: We approached eligible patients using at least one of eight recruitment strategies. We recorded approach dates and the strategy that led to completion of a web-based eligibility questionnaire that was reported during the verbal consent phone call. Study team members conducted monthly sessions using a structured guide to identify recruitment barriers and facilitators. The proportion of participants who reported being recruited by a portal or nonportal strategy was measured as our outcomes. We used Fisher's exact test to compare outcomes by equity variable, and multivariable logistic regression to control for each covariate and adjust effect size estimates. Using grounded theory, we coded and extracted themes regarding recruitment barriers and facilitators. RESULTS: The majority (84.4%) of patients who met study inclusion criteria were patient portal enrollees. Of 6,366 eligible patients who were approached, 627 completed the eligibility questionnaire and were less frequently Hispanic, less frequently Spanish-speaking, and more frequently patient portal enrollees. Of 445 patients who consented to participate, 241 (54.2%) reported completing the eligibility questionnaire after being contacted by a patient portal message. In adjusted analysis, only race (odds ratio [OR]: 0.46, 95% confidence interval [CI]: 0.28-0.77, p = 0.003) and college education (OR: 0.60, 95% CI: 0.39-0.91, p = 0.016) remained significant. Key recruitment barriers included technology issues (e.g., lack of email access) and facilitators included bilingual study staff, Spanish-language recruitment materials, targeted phone calls, and clinician-initiated "1-click" referrals. CONCLUSION: A primarily digital strategy to recruit patients into a digital health trial is unlikely to achieve equitable participation, even in a population overrepresented by patient portal enrollees. Nondigital recruitment methods that address racial and educational disparities and less active portal enrollees are necessary to ensure equity in clinical trial enrollment.

Who Gets (and Who Should Get) a Serious Illness Conversation in the Hospital? An Analysis of Readmission Risk Score in an Electronic Health Record.

Serious Illness Conversations (SICs) explore patients' prognostic awareness, hopes, and worries, and can help establish priorities for their care during and after hospitalization. While identifying patients who benefit from an SIC remains a challenge, this task may be facilitated by use of validated prediction scores available in most commercial electronic health records (EHRs), such as Epic's Readmission Risk Score (RRS). We identified the RRS on admission for all hospital encounters from October 2018 to August 2019 and measured the area under the receiver operating characteristic (AUROC) curve to determine whether RRS could accurately discriminate post discharge 6-month mortality. For encounters with standardized SIC documentation matched in a 1:3 ratio to controls by sex and age (±5 years), we constructed a multivariable, paired logistic regression model and measured the odds of SIC documentation per every 10% absolute increase in RRS. RRS was predictive of 6-month mortality with acceptable discrimination (AUROC .71) and was significantly associated with SIC documentation (adjusted OR 1.42, 95% CI 1.24-1.63). An RRS >28% used to identify patients with post discharge 6-month mortality had a high specificity (89.0%) and negative predictive value (NPV) (97.0%), but low sensitivity (25.2%) and positive predictive value (PPV) (7.9%). RRS may serve as a practical EHR-based screen to exclude patients not requiring an SIC, thereby leaving a smaller cohort to be further evaluated for SIC needs using other validated tools and clinical assessment.

A structured approach to EHR surveillance of diagnostic error in acute care: an exploratory analysis of two institutionally-defined case cohorts.

OBJECTIVES: To test a structured electronic health record (EHR) case review process to identify diagnostic errors (DE) and diagnostic process failures (DPFs) in acute care. METHODS: We adapted validated tools (Safer Dx, Diagnostic Error Evaluation Research [DEER] Taxonomy) to assess the diagnostic process during the hospital encounter and categorized 13 postulated e-triggers. We created two test cohorts of all preventable cases (n=28) and an equal number of randomly sampled non-preventable cases (n=28) from 365 adult general medicine patients who expired and underwent our institution's mortality case review process. After excluding patients with a length of stay of more than one month, each case was reviewed by two blinded clinicians trained in our process and by an expert panel. Inter-rater reliability was assessed. We compared the frequency of DE contributing to death in both cohorts, as well as mean DPFs and e-triggers for DE positive and negative cases within each cohort. RESULTS: Twenty-seven (96.4%) preventable and 24 (85.7%) non-preventable cases underwent our review process. Inter-rater reliability was moderate between individual reviewers (Cohen's kappa 0.41) and substantial with the expert panel (Cohen's kappa 0.74). The frequency of DE contributing to death was significantly higher for the preventable compared to the non-preventable cohort (56% vs. 17%, OR 6.25 [1.68, 23.27], p<0.01). Mean DPFs and e-triggers were significantly and non-significantly higher for DE positive compared to DE negative cases in each cohort, respectively. CONCLUSIONS: We observed substantial agreement among final consensus and expert panel reviews using our structured EHR case review process. DEs contributing to death associated with DPFs were identified in institutionally designated preventable and non-preventable cases. While e-triggers may be useful for discriminating DE positive from DE negative cases, larger studies are required for validation. Our approach has potential to augment institutional mortality case review processes with respect to DE surveillance.

The association of chronic bronchitis and airflow obstruction with lifetime and current farm activities in a sample of rural adults in Iowa.

OBJECTIVE: Farmers have an increased risk for chronic bronchitis and airflow obstruction. The objective of this study was to investigate the association of these health outcomes with farm activities. METHODS: We evaluated the Keokuk County Rural Health Study (KCRHS) enrollment data for farm activities and the two health outcomes chronic bronchitis based on self-reported symptoms and airflow obstruction based on spirometry. We used logistic regression to model the health outcomes, yielding an odds ratio (OR) and 95% confidence interval (95% CI) for farm activities while adjusting for potential confounders and other risk factors. RESULTS: Of the 1234 farmers, 104 (8.4%) had chronic bronchitis, 75 (6.1%) fulfilled the criteria for airflow obstruction, and the two outcomes overlapped by 18 participants. Chronic bronchitis without airflow obstruction (n = 86) had a statistically significant association with crop storage insecticides (OR 3.1, 95% CI 1.6, 6.1) and a low number of years (≤ 3) worked with turkeys (OR 3.3, 95% CI 1.2, 9.4). The latter result should be interpreted with caution because it is based on a small number of cases (n = 5). Airflow obstruction with or without chronic bronchitis (n = 75) was significantly associated with ever working in a hog or chicken confinement setting (OR 2.2, 95% CI 1.0, 4.5). CONCLUSIONS: These results suggest that work with crop storage insecticides or turkeys may increase the risk for chronic bronchitis and work in hog or chicken confinement may increase the risk for airflow obstruction.

User-centered design of a scalable, electronic health record-integrated remote symptom monitoring intervention for patients with asthma and providers in primary care.

OBJECTIVE: To determine user and electronic health records (EHR) integration requirements for a scalable remote symptom monitoring intervention for asthma patients and their providers. METHODS: Guided by the Non-Adoption, Abandonment, Scale-up, Spread, and Sustainability (NASSS) framework, we conducted a user-centered design process involving English- and Spanish-speaking patients and providers affiliated with an academic medical center. We conducted a secondary analysis of interview transcripts from our prior study, new design sessions with patients and primary care providers (PCPs), and a survey of PCPs. We determined EHR integration requirements as part of the asthma app design and development process. RESULTS: Analysis of 26 transcripts (21 patients, 5 providers) from the prior study, 21 new design sessions (15 patients, 6 providers), and survey responses from 55 PCPs (71% of 78) identified requirements. Patient-facing requirements included: 1- or 5-item symptom questionnaires each week, depending on asthma control; option to request a callback; ability to enter notes, triggers, and peak flows; and tips pushed via the app prior to a clinic visit. PCP-facing requirements included a clinician-facing dashboard accessible from the EHR and an EHR inbox message preceding the visit. PCP preferences diverged regarding graphical presentations of patient-reported outcomes (PROs). Nurse-facing requirements included callback requests sent as an EHR inbox message. Requirements were consistent for English- and Spanish-speaking patients. EHR integration required use of custom application programming interfaces (APIs). CONCLUSION: Using the NASSS framework to guide our user-centered design process, we identified patient and provider requirements for scaling an EHR-integrated remote symptom monitoring intervention in primary care. These requirements met the needs of patients and providers. Additional standards for PRO displays and EHR inbox APIs are needed to facilitate spread.