A Mixed Methods Analysis of Standardized Documentation of Serious Illness Conversations Within an Electronic Health Record Module During Hospitalization.

BACKGROUND: Analysis of documented Serious Illness Conversations (SICs) in the inpatient setting can help clinicians align management to address patient and caregiver needs. METHODS: We conducted a mixed methods analysis of the first instance of standardized documentation of a SIC within a structured module among hospitalized general medicine patients from 2018 to 2019. Percentage of documentations that included a description of patient or family understanding of the patient's medical condition and use of radio buttons to answer the "prognostic information shared," "hopes," and "worries" modules are reported. Using grounded theory approach, physicians analyzed free text entries to: "What is important to the patient/family?" and "Recommendations or next steps planned." RESULTS: Out of 5142 patients, 59 patients had a documented SIC. Patient or family understanding of the medical condition(s) was reported in 56 (95%). For "prognostic information shared," the most frequently selected radio buttons were: 49 (83%) incurable disease and 28 (48%) prognosis of weeks to months while those for "hopes" were: 52 (88%) be comfortable and 27 (46%) be at home and for "worries" were: 49 (83%) other physical suffering and 36 (61%) pain. Themes generated from entries to "What's important to patient/family?" included being with loved ones; comfort; mentally and physically present; and reliable care while those for "Recommendations" were coordinating support services; symptom management; and support and communication. CONCLUSIONS: SIC content indicated concern about pain and reliable care suggesting the complex, intensive nature of caring for seriously ill patients and the need to consider SICs earlier in the life course of patients.

Diagnostic Errors in Hospitalized Adults Who Died or Were Transferred to Intensive Care.

Importance: Diagnostic errors contribute to patient harm, though few data exist to describe their prevalence or underlying causes among medical inpatients. Objective: To determine the prevalence, underlying cause, and harms of diagnostic errors among hospitalized adults transferred to an intensive care unit (ICU) or who died. Design, Setting, and Participants: Retrospective cohort study conducted at 29 academic medical centers in the US in a random sample of adults hospitalized with general medical conditions and who were transferred to an ICU, died, or both from January 1 to December 31, 2019. Each record was reviewed by 2 trained clinicians to determine whether a diagnostic error occurred (ie, missed or delayed diagnosis), identify diagnostic process faults, and classify harms. Multivariable models estimated association between process faults and diagnostic error. Opportunity for diagnostic error reduction associated with each fault was estimated using the adjusted proportion attributable fraction (aPAF). Data analysis was performed from April through September 2023. Main Outcomes and Measures: Whether or not a diagnostic error took place, the frequency of underlying causes of errors, and harms associated with those errors. Results: Of 2428 patient records at 29 hospitals that underwent review (mean [SD] patient age, 63.9 [17.0] years; 1107 [45.6%] female and 1321 male individuals [54.4%]), 550 patients (23.0%; 95% CI, 20.9%-25.3%) had experienced a diagnostic error. Errors were judged to have contributed to temporary harm, permanent harm, or death in 436 patients (17.8%; 95% CI, 15.9%-19.8%); among the 1863 patients who died, diagnostic error was judged to have contributed to death in 121 (6.6%; 95% CI, 5.3%-8.2%). In multivariable models examining process faults associated with any diagnostic error, patient assessment problems (aPAF, 21.4%; 95% CI, 16.4%-26.4%) and problems with test ordering and interpretation (aPAF, 19.9%; 95% CI, 14.7%-25.1%) had the highest opportunity to reduce diagnostic errors; similar ranking was seen in multivariable models examining harmful diagnostic errors. Conclusions and Relevance: In this cohort study, diagnostic errors in hospitalized adults who died or were transferred to the ICU were common and associated with patient harm. Problems with choosing and interpreting tests and the processes involved with clinician assessment are high-priority areas for improvement efforts.

The Association of Standardized Documentation of Serious Illness Conversations With Healthcare Utilization in Hospitalized Patients: A Propensity Score Matched Cohort Analysis.

Background: Serious Illness Conversations (SICs) conducted during hospitalization can lead to meaningful patient participation in the decision-making process affecting medical management. The aim of this study is to determine if standardized documentation of a SIC within an institutionally approved EHR module during hospitalization is associated with palliative care consultation, change in code status, hospice enrollment prior to discharge, and 90-day readmissions. Methods: We conducted retrospective analyses of hospital encounters of general medicine patients at a community teaching hospital affiliated with an academic medical center from October 2018 to August 2019. Encounters with standardized documentation of a SIC were identified and matched by propensity score to control encounters without a SIC in a ratio of 1:3. We used multivariable, paired logistic regression and Cox proportional-hazards modeling to assess key outcomes. Results: Of 6853 encounters (5143 patients), 59 (.86%) encounters (59 patients) had standardized documentation of a SIC, and 58 (.85%) were matched to 167 control encounters (167 patients). Encounters with standardized documentation of a SIC had greater odds of palliative care consultation (odds ratio [OR] 60.10, 95% confidence interval [CI] 12.45-290.08, P < .01), a documented code status change (OR 8.04, 95% CI 1.54-42.05, P = .01), and discharge with hospice services (OR 35.07, 95% CI 5.80-212.08, P < .01) compared to matched controls. There was no significant association with 90-day readmissions (adjusted hazard ratio [HR] .88, standard error [SE] .37, P = .73). Conclusions: Standardized documentation of a SIC during hospitalization is associated with palliative care consultation, change in code status, and hospice enrollment.

Achieving diagnostic excellence through prevention and teamwork (ADEPT) study protocol: A multicenter, prospective quality and safety program to improve diagnostic processes in medical inpatients.

BACKGROUND: Few hospitals have built surveillance for diagnostic errors into usual care or used comparative quantitative and qualitative data to understand their diagnostic processes and implement interventions designed to reduce these errors. OBJECTIVES: To build surveillance for diagnostic errors into usual care, benchmark diagnostic performance across sites, pilot test interventions, and evaluate the program's impact on diagnostic error rates. METHODS AND ANALYSIS: Achieving diagnostic excellence through prevention and teamwork (ADEPT) is a multicenter, real-world quality and safety program utilizing interrupted time-series techniques to evaluate outcomes. Study subjects will be a randomly sampled population of medical patients hospitalized at 16 US hospitals who died, were transferred to intensive care, or had a rapid response during the hospitalization. Surveillance for diagnostic errors will occur on 10 events per month per site using a previously established two-person adjudication process. Concurrent reviews of patients who had a qualifying event in the previous week will allow for surveys of clinicians to better understand contributors to diagnostic error, or conversely, examples of diagnostic excellence, which cannot be gleaned from medical record review alone. With guidance from national experts in quality and safety, sites will report and benchmark diagnostic error rates, share lessons regarding underlying causes, and design, implement, and pilot test interventions using both Safety I and Safety II approaches aimed at patients, providers, and health systems. Safety II approaches will focus on cases where diagnostic error did not occur, applying theories of how people and systems are able to succeed under varying conditions. The primary outcome will be the number of diagnostic errors per patient, using segmented multivariable regression to evaluate change in y-intercept and change in slope after initiation of the program. ETHICS AND DISSEMINATION: The study has been approved by the University of California, San Francisco Institutional Review Board (IRB), which is serving as the single IRB. Intervention toolkits and study findings will be disseminated through partners including Vizient, The Joint Commission, and Press-Ganey, and through national meetings, scientific journals, and publications aimed at the general public.

Early Expected Discharge Date Accuracy During Hospitalization: A Multivariable Analysis.

INTRODUCTION: Accurate estimation of an expected discharge date (EDD) early during hospitalization impacts clinical operations and discharge planning. METHODS: We conducted a retrospective study of patients discharged from six general medicine units at an academic medical center in Boston, MA from January 2017 to June 2018. We retrieved all EDD entries and patient, encounter, unit, and provider data from the electronic health record (EHR), and public weather data. We excluded patients who expired, discharged against medical advice, or lacked an EDD within the first 24 h of hospitalization. We used generalized estimating equations in a multivariable logistic regression analysis to model early EDD accuracy (an accurate EDD entered within 24 h of admission), adjusting for all covariates and clustering by patient. We similarly constructed a secondary multivariable model using covariates present upon admission alone. RESULTS: Of 3917 eligible hospitalizations, 890 (22.7%) had at least one accurate early EDD entry. Factors significantly positively associated (OR > 1) with an accurate early EDD included clinician-entered EDD, admit day and discharge day during the work week, and teaching clinical units. Factors significantly negatively associated (OR < 1) with an accurate early EDD included Elixhauser Comorbidity Index ≥ 11 and length of stay of two or more days. C-statistics for the primary and secondary multivariable models were 0.75 and 0.60, respectively. CONCLUSIONS: EDDs entered within the first 24 h of admission were often inaccurate. While several variables from the EHR were associated with accurate early EDD entries, few would be useful for prospective prediction.

Developing, pilot testing, and refining requirements for 3 EHR-integrated interventions to improve diagnostic safety in acute care: a user-centered approach.

Objective: To describe a user-centered approach to develop, pilot test, and refine requirements for 3 electronic health record (EHR)-integrated interventions that target key diagnostic process failures in hospitalized patients. Materials and Methods: Three interventions were prioritized for development: a Diagnostic Safety Column (DSC) within an EHR-integrated dashboard to identify at-risk patients; a Diagnostic Time-Out (DTO) for clinicians to reassess the working diagnosis; and a Patient Diagnosis Questionnaire (PDQ) to gather patient concerns about the diagnostic process. Initial requirements were refined from analysis of test cases with elevated risk predicted by DSC logic compared to risk perceived by a clinician working group; DTO testing sessions with clinicians; PDQ responses from patients; and focus groups with clinicians and patient advisors using storyboarding to model the integrated interventions. Mixed methods analysis of participant responses was used to identify final requirements and potential implementation barriers. Results: Final requirements from analysis of 10 test cases predicted by the DSC, 18 clinician DTO participants, and 39 PDQ responses included the following: DSC configurable parameters (variables, weights) to adjust baseline risk estimates in real-time based on new clinical data collected during hospitalization; more concise DTO wording and flexibility for clinicians to conduct the DTO with or without the patient present; and integration of PDQ responses into the DSC to ensure closed-looped communication with clinicians. Analysis of focus groups confirmed that tight integration of the interventions with the EHR would be necessary to prompt clinicians to reconsider the working diagnosis in cases with elevated diagnostic error (DE) risk or uncertainty. Potential implementation barriers included alert fatigue and distrust of the risk algorithm (DSC); time constraints, redundancies, and concerns about disclosing uncertainty to patients (DTO); and patient disagreement with the care team's diagnosis (PDQ). Discussion: A user-centered approach led to evolution of requirements for 3 interventions targeting key diagnostic process failures in hospitalized patients at risk for DE. Conclusions: We identify challenges and offer lessons from our user-centered design process.

What works in medication reconciliation: an on-treatment and site analysis of the MARQUIS2 study.

BACKGROUND: The second Multicenter Medication Reconciliation Quality Improvement Study demonstrated a marked reduction in medication discrepancies per patient. The aim of the current analysis was to determine the association of patient exposure to each system-level intervention and receipt of each patient-level intervention on these results. METHODS: This study was conducted at 17 North American Hospitals, the study period was 18 months per site, and sites typically adopted interventions after 2-5 months of preintervention data collection. We conducted an on-treatment analysis (ie, an evaluation of outcomes based on patient exposure) of system-level interventions, both at the category level and at the individual component level, based on monthly surveys of implementation site leads at each site (response rate 65%). We then conducted a similar analysis of patient-level interventions, as determined by study pharmacist review of documented activities in the medical record. We analysed the association of each intervention on the adjusted number of medication discrepancies per patient in admission and discharge orders, based on a random sample of up to 22 patients per month per site, using mixed-effects Poisson regression with hospital site as a random effect. We then used a generalised linear mixed-effects model (GLMM) decision tree to determine which patient-level interventions explained the most variance in discrepancy rates. RESULTS: Among 4947 patients, patient exposure to seven of the eight system-level component categories was associated with modest but significant reductions in discrepancy rates (adjusted rate ratios (ARR) 0.75-0.97), as were 15 of the 17 individual system-level intervention components, including hiring, reallocating and training personnel to take a best possible medication history (BPMH) and training personnel to perform discharge medication reconciliation and patient counselling. Receipt of five of seven patient-level interventions was independently associated with large reductions in discrepancy rates, including receipt of a BPMH in the emergency department (ED) by a trained clinician (ARR 0.40, 95% CI 0.37 to 0.43), admission medication reconciliation by a trained clinician (ARR 0.57, 95% CI 0.50 to 0.64) and discharge medication reconciliation by a trained clinician (ARR 0.64, 95% CI 0.57 to 0.73). In GLMM decision tree analyses, patients who received both a BPMH in the ED and discharge medication reconciliation by a trained clinician experienced the lowest discrepancy rates (0.08 per medication per patient). CONCLUSION AND RELEVANCE: Patient-level interventions most associated with reductions in discrepancies were receipt of a BPMH of admitted patients in the ED and admission and discharge medication reconciliation by a trained clinician. System-level interventions were associated with modest reduction in discrepancies for the average patient but are likely important to support patient-level interventions and may reach more patients. These findings can be used to help hospitals and health systems prioritise interventions to improve medication safety during care transitions.

Prevalence and Causes of Diagnostic Errors in Hospitalized Patients Under Investigation for COVID-19.

BACKGROUND: The COVID-19 pandemic required clinicians to care for a disease with evolving characteristics while also adhering to care changes (e.g., physical distancing practices) that might lead to diagnostic errors (DEs). OBJECTIVE: To determine the frequency of DEs and their causes among patients hospitalized under investigation (PUI) for COVID-19. DESIGN: Retrospective cohort. SETTING: Eight medical centers affiliated with the Hospital Medicine ReEngineering Network (HOMERuN). TARGET POPULATION: Adults hospitalized under investigation (PUI) for COVID-19 infection between February and July 2020. MEASUREMENTS: We randomly selected up to 8 cases per site per month for review, with each case reviewed by two clinicians to determine whether a DE (defined as a missed or delayed diagnosis) occurred, and whether any diagnostic process faults took place. We used bivariable statistics to compare patients with and without DE and multivariable models to determine which process faults or patient factors were associated with DEs. RESULTS: Two hundred and fifty-seven patient charts underwent review, of which 36 (14%) had a diagnostic error. Patients with and without DE were statistically similar in terms of socioeconomic factors, comorbidities, risk factors for COVID-19, and COVID-19 test turnaround time and eventual positivity. Most common diagnostic process faults contributing to DE were problems with clinical assessment, testing choices, history taking, and physical examination (all p < 0.01). Diagnostic process faults associated with policies and procedures related to COVID-19 were not associated with DE risk. Fourteen patients (35.9% of patients with errors and 5.4% overall) suffered harm or death due to diagnostic error. LIMITATIONS: Results are limited by available documentation and do not capture communication between providers and patients. CONCLUSION: Among PUI patients, DEs were common and not associated with pandemic-related care changes, suggesting the importance of more general diagnostic process gaps in error propagation.

Providers' and Patients' Perspectives on Diagnostic Errors in the Acute Care Setting.

BACKGROUND: Diagnostic errors (DEs) have been studied extensively in ambulatory care, but less work has been done in the acute care setting. In this study, the authors examined health care providers' and patients' perspectives about the classification of DEs, the main causes and scope of DEs in acute care, the main gaps in current systems, and the need for innovative solutions. METHODS: A qualitative mixed methods study was conducted, including semistructured interviews with health care providers and focus groups with patient advisors. Using grounded theory approach, thematic categories were derived from the interviews and focus groups. RESULTS: The research team conducted interviews with 17 providers and two focus groups with seven patient advisors. Both providers and patient advisors struggled to define and describe DEs in acute care settings. Although participants agreed that DEs pose a significant risk to patient safety, their perception of the frequency of DEs was mixed. Most participants identified communication failures, lack of comfort with diagnostic uncertainty, incorrect clinical evaluation, and cognitive load as key causes of DEs. Most respondents believed that non-information technology (IT) tools and processes (for example, communication improvement strategies) could significantly reduce DEs. CONCLUSION: The study findings represent an important supplement to our understanding of DEs in acute care settings and the advancement of a culture of patient safety in the context of patient-centered care and patient engagement. Health care organizations should consider the key factors identified in this study when trying to create a culture that engages clinicians and patients in reducing DEs.

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.

Development and Testing of an Electronic Health Record-Integrated Patient-Reported Outcome Application and Intervention to Improve Efficiency of Rheumatoid Arthritis Care.

OBJECTIVE: Many patients with rheumatoid arthritis (RA) have difficulty finding clinicians to treat them because of workforce shortages. We developed an app to address this problem by improving care efficiency. The app collects patient-reported outcomes (PROs) and can be used to inform visit timing, potentially reducing the volume of low-value visits. We describe the development process, intervention design, and planned study for testing the app. METHODS: We employed user-centered design, interviewing patients and clinicians, to develop the app. To improve visit efficiency, symptom tracking logic alerts clinicians to PRO trends: worsening PROs generate alerts suggesting an earlier visit, and stable or improving PROs generate notifications that scheduled visits could be delayed. An interrupted time-series analysis with a nonrandomized control population will allow assessment of the impact of the app on visit frequency. RESULTS: Patient interviews identified several of the following needs for effective app and intervention design: the importance of a simple user interface facilitating rapid answering of PROs, the availability of condensed summary information with links to more in-depth answers to common questions regarding RA, and the need for clinicians to discuss the PRO data during visits with patients. Clinician interviews identified the following user needs: PRO data must be easy to view and use during the clinical workflow, and there should be reduced interval visits when PROs are trending worse. Some clinicians believed visits could be delayed for patients with stable PROs, whereas others raised concerns. CONCLUSION: PRO apps may improve care efficiency in rheumatology. Formal evaluation of an integrated PRO RA app is forthcoming.

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.

Comparison of a Voluntary Safety Reporting System to a Global Trigger Tool for Identifying Adverse Events in an Oncology Population.

OBJECTIVE: There is a lack of research on adverse event (AE) detection in oncology patients, despite the propensity for iatrogenic harm. Two common methods include voluntary safety reporting (VSR) and chart review tools, such as the Institute for Healthcare Improvement's Global Trigger Tool (GTT). Our objective was to compare frequency and type of AEs detected by a modified GTT compared with VSR for identifying AEs in oncology patients in a larger clinical trial. METHODS: Patients across 6 oncology units (from July 1, 2013, through May 29, 2015) were randomly selected. Retrospective chart reviews were conducted by a team of nurses and physicians to identify AEs using the GTT. The VSR system was queried by the department of quality and safety of the hospital. Adverse event frequencies, type, and harm code for both methods were compared. RESULTS: The modified GTT detected 0.90 AEs per patient (79 AEs in 88 patients; 95% [0.71-1.12] AEs per patient) that were predominantly medication AEs (53/79); more than half of the AEs caused harm to the patients (41/79, 52%), but only one quarter were preventable (21/79; 27%). The VSR detected 0.24 AEs per patient (21 AEs in 88 patients; 95% [0.15-0.37] AEs per patient), a large plurality of which were medication/intravenous related (8/21); more than half did not cause harm (70%). Only 2% of the AEs (2/100) were detected by both methods. CONCLUSIONS: Neither the modified GTT nor the VSR system alone is sufficient for detecting AEs in oncology patient populations. Further studies exploring methods such as automated AE detection from electronic health records and leveraging patient-reported AEs are needed.

Toward an asthma patient-reported outcome measure for use in digital remote monitoring.

OBJECTIVE: To develop and test a patient-reported outcome measure (PROM) for suitability in digital remote asthma symptom monitoring to identify uncontrolled asthma. METHODS: We modified a 5-item PROM that does not require a license, the asthma control measure (ACM), from a one-month to one-week lookback period, and evaluated it using the 5-item asthma control questionnaire (ACQ-5). We recruited subjects with asthma through MTurk, an online platform. RESULTS: In a sample of 498 subjects, the ACM identified uncontrolled asthma with sensitivity 0.99 and specificity 0.65. The two measures correlated with r = 0.81. CONCLUSION: The ACM modified to a weekly lookback period can differentiate patients with well-controlled asthma from those with uncontrolled asthma. This PROM does not require a license and can be used in digital remote monitoring interventions.

Analyzing diagnostic errors in the acute setting: a process-driven approach.

OBJECTIVES: We describe an approach for analyzing failures in diagnostic processes in a small, enriched cohort of general medicine patients who expired during hospitalization and experienced medical error. Our objective was to delineate a systematic strategy for identifying frequent and significant failures in the diagnostic process to inform strategies for preventing adverse events due to diagnostic error. METHODS: Two clinicians independently reviewed detailed records of purposively sampled cases identified from established institutional case review forums and assessed the likelihood of diagnostic error using the Safer Dx instrument. Each reviewer used the modified Diagnostic Error Evaluation and Research (DEER) taxonomy, revised for acute care (41 possible failure points across six process dimensions), to characterize the frequency of failure points (FPs) and significant FPs in the diagnostic process. RESULTS: Of 166 cases with medical error, 16 were sampled: 13 (81.3%) had one or more diagnostic error(s), and a total of 113 FPs and 30 significant FPs were identified. A majority of significant FPs (63.3%) occurred in "Diagnostic Information and Patient Follow-up" and "Patient and Provider Encounter and Initial Assessment" process dimensions. Fourteen (87.5%) cases had a significant FP in at least one of these dimensions. CONCLUSIONS: Failures in the diagnostic process occurred across multiple dimensions in our purposively sampled cohort. A systematic analytic approach incorporating the modified DEER taxonomy, revised for acute care, offered critical insights into key failures in the diagnostic process that could serve as potential targets for preventative interventions.

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.

Evaluation of electronic health record-integrated digital health tools to engage hospitalized patients in discharge preparation.

OBJECTIVE: To evaluate the effect of electronic health record (EHR)-integrated digital health tools comprised of a checklist and video on transitions-of-care outcomes for patients preparing for discharge. MATERIALS AND METHODS: English-speaking, general medicine patients (>18 years) hospitalized at least 24 hours at an academic medical center in Boston, MA were enrolled before and after implementation. A structured checklist and video were administered on a mobile device via a patient portal or web-based survey at least 24 hours prior to anticipated discharge. Checklist responses were available for clinicians to review in real time via an EHR-integrated safety dashboard. The primary outcome was patient activation at discharge assessed by patient activation (PAM)-13. Secondary outcomes included postdischarge patient activation, hospital operational metrics, healthcare resource utilization assessed by 30-day follow-up calls and administrative data and change in patient activation from discharge to 30 days postdischarge. RESULTS: Of 673 patients approached, 484 (71.9%) enrolled. The proportion of activated patients (PAM level 3 or 4) at discharge was nonsignificantly higher for the 234 postimplementation compared with the 245 preimplementation participants (59.8% vs 56.7%, adjusted OR 1.23 [0.38, 3.96], P = .73). Postimplementation participants reported 3.75 (3.02) concerns via the checklist. Mean length of stay was significantly higher for postimplementation compared with preimplementation participants (10.13 vs 6.21, P < .01). While there was no effect on postdischarge outcomes, there was a nonsignificant decrease in change in patient activation within participants from pre- to postimplementation (adjusted difference-in-difference of -16.1% (9.6), P = .09). CONCLUSIONS: EHR-integrated digital health tools to prepare patients for discharge did not significantly increase patient activation and was associated with a longer length of stay. While issues uncovered by the checklist may have encouraged patients to inquire about their discharge preparedness, other factors associated with patient activation and length of stay may explain our observations. We offer insights for using PAM-13 in context of real-world health-IT implementations. TRIAL REGISTRATION: NIH US National Library of Medicine, NCT03116074, clinicaltrials.gov.

The Effects of a Multifaceted Intervention to Improve Care Transitions Within an Accountable Care Organization: Results of a Stepped-Wedge Cluster-Randomized Trial.

BACKGROUND: Transitions from hospital to the ambulatory setting are high risk for patients in terms of adverse events, poor clinical outcomes, and readmission. OBJECTIVES: To develop, implement, and refine a multifaceted care transitions intervention and evaluate its effects on postdischarge adverse events. DESIGN, SETTING, AND PARTICIPANTS: Two-arm, single-blind (blinded outcomes assessor), stepped-wedge, cluster-randomized clinical trial. Participants were 1,679 adult patients who belonged to one of 17 primary care practices and were admitted to a medical or surgical service at either of two participating hospitals within a pioneer accountable care organization (ACO). INTERVENTIONS: Multicomponent intervention in the 30 days following hospitalization, including inpatient pharmacist-led medication reconciliation, coordination of care between an inpatient "discharge advocate" and a primary care "responsible outpatient clinician," postdischarge phone calls, and postdischarge primary care visit. MAIN OUTCOMES AND MEASURES: The primary outcome was rate of postdischarge adverse events, as assessed by a 30-day postdischarge phone call and medical record review and adjudicated by two blinded physician reviewers. Secondary outcomes included preventable adverse events, new or worsening symptoms after discharge, and 30-day nonelective hospital readmission. RESULTS: Among patients included in the study, 692 were assigned to usual care and 987 to the intervention. Patients in the intervention arm had a 45% relative reduction in postdischarge adverse events (18 vs 23 events per 100 patients; adjusted incidence rate ratio, 0.55; 95% CI, 0.35-0.84). Significant reductions were also seen in preventable adverse events and in new or worsening symptoms, but there was no difference in readmission rates. CONCLUSION: A multifaceted intervention was associated with a significant reduction in postdischarge adverse events but no difference in 30-day readmission rates.