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.

Interactive Digital Health Tools to Engage Patients and Caregivers in Discharge Preparation: Implementation Study.

BACKGROUND: Poor discharge preparation during hospitalization may lead to adverse events after discharge. Checklists and videos that systematically engage patients in preparing for discharge have the potential to improve safety, especially when integrated into clinician workflow via the electronic health record (EHR). OBJECTIVE: This study aims to evaluate the implementation of a suite of digital health tools integrated with the EHR to engage hospitalized patients, caregivers, and their care team in preparing for discharge. METHODS: We used the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework to identify pertinent research questions related to implementation. We iteratively refined patient and clinician-facing intervention components using a participatory process involving end users and institutional stakeholders. The intervention was implemented at a large academic medical center from December 2017 to July 2018. Patients who agreed to participate were coached to watch a discharge video, complete a checklist assessing discharge readiness, and request postdischarge text messaging with a physician 24 to 48 hours before their expected discharge date, which was displayed via a patient portal and bedside display. Clinicians could view concerns reported by patients based on their checklist responses in real time via a safety dashboard integrated with the EHR and choose to open a secure messaging thread with the patient for up to 7 days after discharge. We used mixed methods to evaluate our implementation experience. RESULTS: Of 752 patient admissions, 510 (67.8%) patients or caregivers participated: 416 (55.3%) watched the video and completed the checklist, and 94 (12.5%) completed the checklist alone. On average, 4.24 concerns were reported per each of the 510 checklist submissions, most commonly about medications (664/2164, 30.7%) and follow-up (656/2164, 30.3%). Of the 510 completed checklists, a member of the care team accessed the safety dashboard to view 210 (41.2%) patient-reported concerns. For 422 patient admissions where postdischarge messaging was available, 141 (33.4%) patients requested this service; of these, a physician initiated secure messaging for 3 (2.1%) discharges. Most patient survey participants perceived that the intervention promoted self-management and communication with their care team. Patient interview participants endorsed gaps in communication with their care team and thought that the video and checklist would be useful closer toward discharge. Clinicians participating in focus groups perceived the value for patients but suggested that low awareness and variable workflow regarding the intervention, lack of technical optimization, and inconsistent clinician leadership limited the use of clinician-facing components. CONCLUSIONS: A suite of EHR-integrated digital health tools to engage patients, caregivers, and clinicians in discharge preparation during hospitalization was feasible, acceptable, and valuable; however, important challenges were identified during implementation. We offer strategies to address implementation barriers and promote adoption of these tools. TRIAL REGISTRATION: ClinicalTrials.gov NCT03116074; https://clinicaltrials.gov/ct2/show/NCT03116074.

Screening for Adverse Drug Events: a Randomized Trial of Automated Calls Coupled with Phone-Based Pharmacist Counseling.

BACKGROUND: Medication adverse events are important and common yet are often not identified by clinicians. We evaluated an automated telephone surveillance system coupled with transfer to a live pharmacist to screen potentially drug-related symptoms after newly starting medications for four common primary care conditions: hypertension, diabetes, depression, and insomnia. METHODS: Cluster randomized trial with automated calls to eligible patients at 1 and 4 months after starting target drugs from intervention primary care clinics compared to propensity-matched patients from control clinics. Primary and secondary outcomes were physician documentation of any adverse effects associated with newly prescribed target medication, and whether the medication was discontinued and, if yes, whether the reason for stopping was an adverse effect. RESULTS: Of 4876 eligible intervention clinic patients who were contacted using automated calls, 776 (15.1%) responded and participated in the automated call. Based on positive symptom responses or request to speak to a pharmacist, 320 patients were transferred to the pharmacist and discussed 1021 potentially drug-related symptoms. Of these, 188 (18.5%) were assessed as probably and 479 (47.1%) as possibly related to the medication. Compared to a propensity-matched cohort of control clinic patients, intervention patients were significantly more likely to have adverse effects documented in the medical record by a physician (277 vs. 164 adverse effects, p < 0.0001, and 177 vs. 122 patients discontinued with documented adverse effects, p < 0.0001). DISCUSSION: Systematic automated telephone outreach monitoring coupled with real-time phone referral to a pharmacist identified a substantial number of previously unidentified potentially drug-related symptoms, many of which were validated as probably or possibly related to the drug by the pharmacist or their physicians. Multiple challenges were encountered using the interactive voice response (IVR) automated calling system, suggesting that other approaches may need to be considered and evaluated. TRIAL REGISTRATION: ClinicalTrials.gov : NCT02087293.

Acute Care Patient Portal Intervention: Portal Use and Patient Activation.

BACKGROUND: Patient-facing health information technology (HIT) tools, such as patient portals, are recognized as a potential mechanism to facilitate patient engagement and patient-centered care, yet the use of these tools remains limited in the hospital setting. Although research in this area is growing, it is unclear how the use of acute care patient portals might affect outcomes, such as patient activation. OBJECTIVE: The aim of this study was to describe the use of an acute care patient portal and investigate its association with patient and care partner activation in the hospital setting. METHODS: We implemented an acute care patient portal on 6 acute care units over an 18-month period. We investigated the characteristics of the users (patients and their care partners) of the patient portal, as well as their use of the portal. This included the number of visits to each page, the number of days used, the length of the user's access period, and the average percent of days used during the access period. Patient and care partner activation was assessed using the short form of the patient activation measure (PAM-13) and the caregiver patient activation measure (CG-PAM). Comparisons of the activation scores were performed using propensity weighting and robust weighted linear regression. RESULTS: Of the 2974 randomly sampled patients, 59.01% (1755/2974) agreed to use the acute care patient portal. Acute care patient portal enrollees were younger, less sick, less likely to have Medicare as their insurer, and more likely to use the Partners Healthcare enterprise ambulatory patient portal (Patient Gateway). The most used features of the acute care patient portal were the laboratory test results, care team information, and medication list. Most users accessed the portal between 1 to 4 days during their hospitalization, and the average number of days used (logged in at least once per day) was 1.8 days. On average, users accessed the portal 42.69% of the hospital days during which it was available. There was significant association with patient activation on the neurology service (P<.001) and medicine service (P=.01), after the introduction of HIT tools and the acute care patient portal, but not on the oncology service. CONCLUSIONS: Portal users most often accessed the portal to view their clinical information, though portal usage was limited to only the first few days of enrollment. We found an association between the use of the portal and HIT tools with improved levels of patient activation. These tools may help facilitate patient engagement and improve outcomes when fully utilized by patients and care partners. Future study should leverage usage metrics to describe portal use and assess the impact of HIT tools on specific outcome measures in the hospital setting.

Lessons Learned in Implementing a Chronic Opioid Therapy Management System.

OBJECTIVES: Opioid misuse has resulted in significant morbidity and mortality in the United States, and safer opioid use represents an important challenge in the primary care setting. This article describes a research collaborative of health service researchers, systems engineers, and clinicians seeking to improve processes for safer chronic opioid therapy management in an academic primary care center. We present implementation results and lessons learned along with an intervention toolkit that others may consider using within their organization. METHODS: Using iterative improvement lifecycles and systems engineering principles, we developed a risk-based workflow model for patients on chronic opioids. Two key safe opioid use process metrics-percent of patients with recent opioid treatment agreements and urine drug tests-were identified, and processes to improve these measures were designed, tested, and implemented. Focus groups were conducted after the conclusion of implementation, with barriers and lessons learned identified via thematic analysis. RESULTS: Initial surveys revealed a lack of knowledge regarding resources available to patients and prescribers in the primary care clinic. In addition, 18 clinicians (69%) reported largely "inheriting" (rather than initiating) their chronic opioid therapy patients. We tracked 68 patients over a 4-year period. Although process measures improved, full adherence was not achieved for the entire population. Barriers included team structure, the evolving opioid environment, and surveillance challenges, along with disruptions resulting from the 2019 novel coronavirus. CONCLUSIONS: Safe primary care opioid prescribing requires ongoing monitoring and management in a complex environment. The application of a risk-based approach is possible but requires adaptability and redundancies to be reliable.

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.

Assessing the cognitive and work load of an inpatient safety dashboard in the context of opioid management.

For health information technology to realize its potential to improve flow, care, and patient safety, applications should be intuitive to use and burden neutral for frontline clinicians. We assessed the impact of a patient safety dashboard on clinician cognitive and work load within a simulated information-seeking task for safe inpatient opioid medication management. Compared to use of an electronic health record for the same task, the dashboard was associated with significantly reduced time on task, mouse clicks, and mouse movement (each p < 0.001), with no significant increases in cognitive load nor task inaccuracy. Cognitive burden was higher for users with less experience, possibly partly attributable to usability issues identified during this study. Findings underscore the importance of assessing the usability, cognitive, and work load analysis during the design and implementation of health information technology applications.

Lessons learned implementing a complex and innovative patient safety learning laboratory project in a large academic medical center.

OBJECTIVE: The objective of this paper is to share challenges, recommendations, and lessons learned regarding the development and implementation of a Patient Safety Learning Laboratory (PSLL) project, an innovative and complex intervention comprised of a suite of Health Information Technology (HIT) tools integrated with a newly implemented Electronic Health Record (EHR) vendor system in the acute care setting at a large academic center. MATERIALS AND METHODS: The PSLL Administrative Core engaged stakeholders and study personnel throughout all phases of the project: problem analysis, design, development, implementation, and evaluation. Implementation challenges and recommendations were derived from direct observations and the collective experience of PSLL study personnel. RESULTS: The PSLL intervention was implemented on 12 inpatient units during the 18-month study period, potentially impacting 12,628 patient admissions. Challenges to implementation included stakeholder engagement, project scope/complexity, technology/governance, and team structure. Recommendations to address each of these challenges were generated, some enacted during the trial, others as lessons learned for future iterative refinements of the intervention and its implementation. CONCLUSION: Designing, implementing, and evaluating a suite of tools integrated within a vendor EHR to improve patient safety has a variety of challenges. Keys to success include continuous stakeholder engagement, involvement of systems and human factors engineers within a multidisciplinary team, an iterative approach to user-centered design, and a willingness to think outside of current workflows and processes to change health system culture around adverse event prevention.

Use, Perceived Usability, and Barriers to Implementation of a Patient Safety Dashboard Integrated within a Vendor EHR.

BACKGROUND: Preventable adverse events continue to be a threat to hospitalized patients. Clinical decision support in the form of dashboards may improve compliance with evidence-based safety practices. However, limited research describes providers' experiences with dashboards integrated into vendor electronic health record (EHR) systems. OBJECTIVE: This study was aimed to describe providers' use and perceived usability of the Patient Safety Dashboard and discuss barriers and facilitators to implementation. METHODS: The Patient Safety Dashboard was implemented in a cluster-randomized stepped wedge trial on 12 units in neurology, oncology, and general medicine services over an 18-month period. Use of the Dashboard was tracked during the implementation period and analyzed in-depth for two 1-week periods to gather a detailed representation of use. Providers' perceptions of tool usability were measured using the Health Information Technology Usability Evaluation Scale (rated 1-5). Research assistants conducted field observations throughout the duration of the study to describe use and provide insight into tool adoption. RESULTS: The Dashboard was used 70% of days the tool was available, with use varying by role, service, and time of day. On general medicine units, nurses logged in throughout the day, with many logins occurring during morning rounds, when not rounding with the care team. Prescribers logged in typically before and after morning rounds. On neurology units, physician assistants accounted for most logins, accessing the Dashboard during daily brief interdisciplinary rounding sessions. Use on oncology units was rare. Satisfaction with the tool was highest for perceived ease of use, with attendings giving the highest rating (4.23). The overall lowest rating was for quality of work life, with nurses rating the tool lowest (2.88). CONCLUSION: This mixed methods analysis provides insight into the use and usability of a dashboard tool integrated within a vendor EHR and can guide future improvements and more successful implementation of these types of tools.