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.

Investigating racial and ethnic disparities in interhospital transfer within an academic integrated healthcare system: A matched cohort study.

The presence of racial and ethnic disparities in interhospital transfer (IHT) within integrated healthcare systems has not been fully explored. We matched Black and Latinx patients admitted to community hospitals in our integrated healthcare system between June 2015 and December 2019 to White patients by origin hospital, age, time of year, and disease severity. We performed conditional logistic regression models to determine if race or ethnicity was associated with IHT in one of the tertiary academic medical centers in the system, adjusting for covariates. The sample contained 107,895 admissions (82.6% White, 7.8% Black, and 9.6% Latinx). Transfer rates were 2.2% versus 2.2% after the Black/White match and 1.8% versus 1.8% after the Latinx/White match. After adjusting for covariates, there was no association between race or ethnicity and IHT (Black vs. White odds ratio [OR]: 0.87, 95% confidence interval [CI]: 0.72-1.07; Latinx vs. White OR: 1.05, 95% CI: 0.79-1.40). This may be due to reduced barriers to transfer with an integrated healthcare system.

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.

Intravenous Medication Administration Safety with Smart Infusion Pumps in the Neonatal Intensive Care Unit: An Observational Study.

INTRODUCTION: Infants in the neonatal intensive care unit (NICU) are among the most vulnerable patient populations and medication errors are a significant source of risk and harm to neonates. Smart infusion pumps have been implemented to support the safe medication administration process; however, the effect of using smart infusion pumps on medication safety in the NICU is still unclear. METHODS: We conducted an observational study with a prospective point-prevalence approach to investigate intravenous (IV) medication administration errors in the NICU at one academic medical center in the USA. Observations were conducted in 48 days in a 3-month data collection period in 2019. RESULTS: We observed a total of 441 patients with 905 IV medication administrations during the data collection period. The total number of errors was 130 (14.4 per 100 administrations). Of these, the most frequent errors were selecting the wrong drug library entry (5.3 per 100 administrations), unauthorized medication (0.7 per 100 administrations), and wrong dose (0.6 per 100 administrations). Sixty-eight errors (7.5 per 100 administrations) were unlikely to cause harm despite reaching the patient (category C errors), while the rest did not reach the patient. CONCLUSION: We identified the medication errors, which was unique to NICU populations, but no harm to the patients were identified. Most errors occurred due to a lack of compliance of using smart pump technology; therefore, potential exists to maximize safety related to medication administration practices in the NICU through hospital policy change and increasing adherence to appropriate use of smart pump technology.

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.

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.

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.

The Impact of Smart Pump Interoperability on Errors in Intravenous Infusion Administrations: A Multihospital Before and After Study.

OBJECTIVE: The objective of this study was to assess the frequency, type, and severity of errors associated with intravenous medication administration before and after smart pump interoperability. METHODS: We conducted an observational study at a community healthcare system before and after implementing smart pump interoperability. Point prevalence methodology was used to collect data on medication administration and errors in adult inpatient settings. RESULTS: Observations were completed for 350 infusions preintervention (178 patients) and 367 postintervention (200 patients). Total errors significantly decreased from 401 (114.6 per 100 infusions) to 354 (96.5 per 100 infusions, P = 0.02). Administration errors decreased from 144 (41.1 per 100 infusions) to 119 (32.4 per 100 infusions, P = 0.12). Expired medication errors significantly reduced from 11 (3.1 per 100 infusions) to 2 (0.5 per 100 infusions, P = 0.02). Errors involving high-risk medications significantly reduced from 45 (12.8 per 100 infusions) to 25 (6.8 per 100 infusions, P = 0.01). Errors involving continuous medications significantly reduced from 44 (12.6 per 100 infusions) to 22 (6.0 per 100 infusions, P = 0.005). When comparing programming type, manual programming resulted in 115 (77.2%) of administration and user documentation errors compared with 34 errors (22.8%) that occurred when autoprogramming was used. Of these, errors involving high-risk medications reduced from 21 (84.0%) to 4 (16.0%) after using autoprogramming. CONCLUSIONS: Smart pump interoperability resulted in a 16% reduction in medication administration errors. Despite using dose error reduction software and autoprogramming, some types of errors persisted. Further studies are needed to understand how technology use can be optimized.

Reducing ICU Utilization, Length of Stay, and Cost by Optimizing the Clinical Use of Continuous Monitoring System Technology in the Hospital.

BACKGROUND: Continuous monitoring system technology (CMST) aids in earlier detection of deterioration of hospitalized patients, but whether improved outcomes are sustainable is unknown. METHODS: This interrupted time series evaluation explored whether optimized clinical use of CMST was associated with sustained improvement in intensive care unit (ICU) utilization, hospital length of stay, cardiac arrest rates, code blue events, mortality, and cost across multiple adult acute care units. RESULTS: A total of 20,320 patients in the postoptimized use cohort compared with 16,781 patients in the preoptimized use cohort had a significantly reduced ICU transfer rate (1.73% vs 2.25%, P = .026) corresponding to 367.11 ICU days saved over a 2-year period, generating an estimated cost savings of more than $2.3 million. Among patients who transferred to the ICU, hospital length of stay was decreased (8.37 vs 9.64 days, P = .004). Cardiac arrest, code blue, and mortality rates did not differ significantly. CONCLUSION: Opportunities exist to promote optimized adoption and use of CMST at acute care facilities to sustainably improve clinical outcomes and reduce cost.

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.

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.

Design of MARQUIS2: study protocol for a mentored implementation study of an evidence-based toolkit to improve patient safety through medication reconciliation.

BACKGROUND: The first Multi-center Medication Reconciliation Quality Improvement Study (MARQUIS1) demonstrated that implementation of a medication reconciliation best practices toolkit decreased total unintentional medication discrepancies in five hospitals. We sought to implement the MARQUIS toolkit in more diverse hospitals, incorporating lessons learned from MARQUIS1. METHODS: MARQUIS2 is a pragmatic, mentored implementation QI study which collected clinical and implementation outcomes. Sites implemented a revised toolkit, which included interventions from these domains: 1) best possible medication history (BPMH)-taking; 2) discharge medication reconciliation and patient/caregiver counseling; 3) identifying and defining clinician roles and responsibilities; 4) risk stratification; 5) health information technology improvements; 6) improved access to medication sources; 7) identification and correction of real-time discrepancies; and, 8) stakeholder engagement. Eight hospitalists mentored the sites via one site visit and monthly phone calls over the 18-month intervention period. Each site's local QI team assessed opportunities to improve, implemented at least one of the 17 toolkit components, and accessed a variety of resources (e.g. implementation manual, webinars, and workshops). Outcomes to be assessed will include unintentional medication discrepancies per patient. DISCUSSION: A mentored multi-center medication reconciliation QI initiative using a best practices toolkit was successfully implemented across 18 medical centers. The 18 participating sites varied in size, teaching status, location, and electronic health record (EHR) platform. We introduce barriers to implementation and lessons learned from MARQUIS1, such as the importance of utilizing dedicated, trained medication history takers, simple EHR solutions, clarifying roles and responsibilities, and the input of patients and families when improving medication reconciliation.

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.

Patient safety after implementation of a coproduced family centered communication programme: multicenter before and after intervention study

OBJECTIVE: To determine whether medical errors, family experience, and communication processes improved after implementation of an intervention to standardize the structure of healthcare provider-family communication on family centered rounds. DESIGN: Prospective, multicenter before and after intervention study. SETTING: Pediatric inpatient units in seven North American hospitals, 17 December 2014 to 3 January 2017. PARTICIPANTS: All patients admitted to study units (3106 admissions, 13171 patient days); 2148 parents or caregivers, 435 nurses, 203 medical students, and 586 residents. INTERVENTION: Families, nurses, and physicians coproduced an intervention to standardize healthcare provider-family communication on ward rounds ("family centered rounds"), which included structured, high reliability communication on bedside rounds emphasizing health literacy, family engagement, and bidirectional communication; structured, written real-time summaries of rounds; a formal training programme for healthcare providers; and strategies to support teamwork, implementation, and process improvement. MAIN OUTCOME MEASURES: Medical errors (primary outcome), including harmful errors (preventable adverse events) and non-harmful errors, modeled using Poisson regression and generalized estimating equations clustered by site; family experience; and communication processes (eg, family engagement on rounds). Errors were measured via an established systematic surveillance methodology including family safety reporting. RESULTS: The overall rate of medical errors (per 1000 patient days) was unchanged (41.2 (95% confidence interval 31.2 to 54.5) pre-intervention v 35.8 (26.9 to 47.7) post-intervention, P=0.21), but harmful errors (preventable adverse events) decreased by 37.9% (20.7 (15.3 to 28.1) v 12.9 (8.9 to 18.6), P=0.01) post-intervention. Non-preventable adverse events also decreased (12.6 (8.9 to 17.9) v 5.2 (3.1 to 8.8), P=0.003). Top box (eg, "excellent") ratings for six of 25 components of family reported experience improved; none worsened. Family centered rounds occurred more frequently (72.2% (53.5% to 85.4%) v 82.8% (64.9% to 92.6%), P=0.02). Family engagement 55.6% (32.9% to 76.2%) v 66.7% (43.0% to 84.1%), P=0.04) and nurse engagement (20.4% (7.0% to 46.6%) v 35.5% (17.0% to 59.6%), P=0.03) on rounds improved. Families expressing concerns at the start of rounds (18.2% (5.6% to 45.3%) v 37.7% (17.6% to 63.3%), P=0.03) and reading back plans (4.7% (0.7% to 25.2%) v 26.5% (12.7% to 7.3%), P=0.02) increased. Trainee teaching and the duration of rounds did not change significantly. CONCLUSIONS: Although overall errors were unchanged, harmful medical errors decreased and family experience and communication processes improved after implementation of a structured communication intervention for family centered rounds coproduced by families, nurses, and physicians. Family centered care processes may improve safety and quality of care without negatively impacting teaching or duration of rounds. TRIAL REGISTRATION: ClinicalTrials.gov NCT02320175.

A Multi-hospital Before-After Observational Study Using a Point-Prevalence Approach with an Infusion Safety Intervention Bundle to Reduce Intravenous Medication Administration Errors.

INTRODUCTION: We previously found a high rate of errors in the administration of intravenous medications using smart infusion pumps. OBJECTIVES/DESIGN: An infusion safety intervention bundle was developed in response to the high rate of identified errors. A before-after observational study with a prospective point-prevalence approach was conducted in nine hospitals to measure the preliminary effects of the intervention. MAIN OUTCOME MEASURES: Primary outcome measures were overall errors and medication errors, with the secondary outcome defined as potentially harmful error rates. RESULTS: We assessed a total of 418 patients with 972 medication administrations in the pre-intervention period and 422 patients with 1059 medication administrations in the post-intervention period. The overall error rate fell from 146 to 123 per 100 medication administrations (p < 0.0001), and the medication error rate also decreased from 39 to 29 per 100 medication administrations (p = 0.001). However, there was no significant change in the potentially harmful error rate (from 0.5 to 0.8 per 100 medication administrations, p = 0.37). An intervention component aiming to reduce labeling-not-completed errors was effective in reducing targeted error rates, but other components of the intervention bundle did not show significant improvement in the targeted errors. CONCLUSION: Development and implementation of the intervention bundle was successful at reducing overall and medication error rates, but some errors remained and the potentially harmful error rate did not change. The error-rate reductions were not always correlated with the specific individual interventions. Further investigation is needed to identify the best strategies to reduce the remaining errors. CLINICAL TRIALS REGISTRATION: Registered at ClinicalTrials.gov, identifier: NCT02359734.

Randomized Trial of Reducing Ambulatory Malpractice and Safety Risk: Results of the Massachusetts PROMISES Project.

OBJECTIVE: Evaluate application of quality improvement approaches to key ambulatory malpractice risk and safety areas. STUDY SETTING: In total, 25 small-to-medium-sized primary care practices (16 intervention; 9 control) in Massachusetts. STUDY DESIGN: Controlled trial of a 15-month intervention including exposure to a learning network, webinars, face-to-face meetings, and coaching by improvement advisors targeting "3+1" high-risk domains: test result, referral, and medication management plus culture/communication issues evaluated by survey and chart review tools. DATA COLLECTION METHODS: Chart reviews conducted at baseline and postintervention for intervention sites. Staff and patient survey data collected at baseline and postintervention for intervention and control sites. PRINCIPAL FINDINGS: Chart reviews demonstrated significant improvements in documentation of abnormal results, patient notification, documentation of an action or treatment plan, and evidence of a completed plan (all P<0.001). Mean days between laboratory test date and evidence of completed action/treatment plan decreased by 19.4 days (P<0.001). Staff surveys showed modest but nonsignificant improvement for intervention practices relative to controls overall and for the 3 high-risk domains that were the focus of PROMISES. CONCLUSIONS: A consortium of stakeholders, quality improvement tools, coaches, and learning network decreased selected ambulatory safety risks often seen in malpractice claims.

Prospective Evaluation of a Multifaceted Intervention to Improve Outcomes in Intensive Care: The Promoting Respect and Ongoing Safety Through Patient Engagement Communication and Technology Study.

OBJECTIVES: Studies comprehensively assessing interventions to improve team communication and to engage patients and care partners in ICUs are lacking. This study examines the effectiveness of a patient-centered care and engagement program in the medical ICU. DESIGN: Prospective intervention study. SETTING: Medical ICUs at large tertiary care center. PATIENTS: Two thousand one hundred five patient admissions (1,030 before and 1,075 during the intervention) from July 2013 to May 2014 and July 2014 to May 2015. INTERVENTIONS: Structured patient-centered care and engagement training program and web-based technology including ICU safety checklist, tools to develop shared care plan, and messaging platform. Patient and care partner access to online portal to view health information, participate in the care plan, and communicate with providers. MEASUREMENTS AND MAIN RESULTS: Primary outcome was aggregate adverse event rate. Secondary outcomes included patient and care partner satisfaction, care plan concordance, and resource utilization. We included 2,105 patient admissions, (1,030 baseline and 1,075 during intervention periods). The aggregate rate of adverse events fell 29%, from 59.0 per 1,000 patient days (95% CI, 51.8-67.2) to 41.9 per 1,000 patient days (95% CI, 36.3-48.3; p < 0.001), during the intervention period. Satisfaction improved markedly from an overall hospital rating of 71.8 (95% CI, 61.1-82.6) to 93.3 (95% CI, 88.2-98.4; p < 0.001) for patients and from 84.3 (95% CI, 81.3-87.3) to 90.0 (95% CI, 88.1-91.9; p < 0.001) for care partners. No change in care plan concordance or resource utilization. CONCLUSIONS: Implementation of a structured team communication and patient engagement program in the ICU was associated with a reduction in adverse events and improved patient and care partner satisfaction.

Families as Partners in Hospital Error and Adverse Event Surveillance.

Importance: Medical errors and adverse events (AEs) are common among hospitalized children. While clinician reports are the foundation of operational hospital safety surveillance and a key component of multifaceted research surveillance, patient and family reports are not routinely gathered. We hypothesized that a novel family-reporting mechanism would improve incident detection. Objective: To compare error and AE rates (1) gathered systematically with vs without family reporting, (2) reported by families vs clinicians, and (3) reported by families vs hospital incident reports. Design, Setting, and Participants: We conducted a prospective cohort study including the parents/caregivers of 989 hospitalized patients 17 years and younger (total 3902 patient-days) and their clinicians from December 2014 to July 2015 in 4 US pediatric centers. Clinician abstractors identified potential errors and AEs by reviewing medical records, hospital incident reports, and clinician reports as well as weekly and discharge Family Safety Interviews (FSIs). Two physicians reviewed and independently categorized all incidents, rating severity and preventability (agreement, 68%-90%; κ, 0.50-0.68). Discordant categorizations were reconciled. Rates were generated using Poisson regression estimated via generalized estimating equations to account for repeated measures on the same patient. Main Outcomes and Measures: Error and AE rates. Results: Overall, 746 parents/caregivers consented for the study. Of these, 717 completed FSIs. Their median (interquartile range) age was 32.5 (26-40) years; 380 (53.0%) were nonwhite, 566 (78.9%) were female, 603 (84.1%) were English speaking, and 380 (53.0%) had attended college. Of 717 parents/caregivers completing FSIs, 185 (25.8%) reported a total of 255 incidents, which were classified as 132 safety concerns (51.8%), 102 nonsafety-related quality concerns (40.0%), and 21 other concerns (8.2%). These included 22 preventable AEs (8.6%), 17 nonharmful medical errors (6.7%), and 11 nonpreventable AEs (4.3%) on the study unit. In total, 179 errors and 113 AEs were identified from all sources. Family reports included 8 otherwise unidentified AEs, including 7 preventable AEs. Error rates with family reporting (45.9 per 1000 patient-days) were 1.2-fold (95% CI, 1.1-1.2) higher than rates without family reporting (39.7 per 1000 patient-days). Adverse event rates with family reporting (28.7 per 1000 patient-days) were 1.1-fold (95% CI, 1.0-1.2; P = .006) higher than rates without (26.1 per 1000 patient-days). Families and clinicians reported similar rates of errors (10.0 vs 12.8 per 1000 patient-days; relative rate, 0.8; 95% CI, .5-1.2) and AEs (8.5 vs 6.2 per 1000 patient-days; relative rate, 1.4; 95% CI, 0.8-2.2). Family-reported error rates were 5.0-fold (95% CI, 1.9-13.0) higher and AE rates 2.9-fold (95% CI, 1.2-6.7) higher than hospital incident report rates. Conclusions and Relevance: Families provide unique information about hospital safety and should be included in hospital safety surveillance in order to facilitate better design and assessment of interventions to improve safety.