The Joint Commission's Ongoing Professional Practice Evaluation Process: Costly, Ineffective, and Potentially Harmful to Safety Culture.

OBJECTIVE: To evaluate the estimated labor costs and effectiveness of Ongoing Professional Practice Evaluation (OPPE) processes at identifying outlier performers in a large sample of providers across multiple health care systems and to extrapolate costs and effectiveness nationally. METHODS: Six hospital systems partnered to evaluate their labor expenses related to conducting OPPE. Estimates for mean labor hours and wages were created for the following: data analysts, medical staff office professionals, department physician leaders, and administrative assistants. The total number of outlier performers who were identified by OPPE metrics alone and that resulted in lack of renewal, limitation, or revoking of hospital privileges during the past annual OPPE cycle (2022) was recorded. National costs of OPPE were extrapolated. Literature review of the effect of OPPE on safety culture in radiology was performed. RESULTS: The evaluated systems had 12,854 privileged providers evaluated by OPPE. The total estimated annual recurring labor cost per provider was $50.20. Zero of 12,854 providers evaluated were identified as outlier performers solely through the OPPE process. The total estimated annual recurring cost of administering OPPE nationally was $78.54 million. In radiology over the past 15 years, the use of error rates based on score-based peer review as an OPPE metric has been perceived as punitive and had an adverse effect on safety culture. CONCLUSION: OPPE is expensive to administer, inefficient at identifying outlier performers, diverts human resources away from potentially more effective improvement work, and has been associated with an adverse impact on safety culture in radiology.

Serious Experience Events: Applying Patient Safety Concepts to Improve Patient Experience.

Pediatric healthcare systems have successfully decreased patient harm and improved patient safety by adopting standardized definitions, processes, and infrastructure for serious safety events (SSEs). We have adopted those patient safety concepts and used that infrastructure to identify and create action plans to mitigate events in which patient experience is severely compromised. We define those events as serious experience events (SEEs). The purpose of this research brief is to describe SEE definitions, infrastructure used to evaluate potential SEEs, and creation of action plans as well as share our preliminary experiences with the approach.

The approach to improving patient experience at children's hospitals: a primer for pediatric radiologists.

Increasing attention is being given to improving patient experience in health care. Most children's hospitals have a patient experience office or team that champions and measures patient experience and partners with operations to optimize performance in this area. We outline the activities that our patient experience team undertakes at our pediatric health system to advocate for, measure and improve the experience of our patients and families. The framework we propose for such activities includes those that are proactive in improving patient experience as well as those that are reactive to when patients and families have had a poor experience. Those reactive practices are often centered on the management of patient complaints and grievances and early intervention into patient complaints so that they do not become grievances.

Automatic detection of hand hygiene using computer vision technology.

OBJECTIVE: Hand hygiene is essential for preventing hospital-acquired infections but is difficult to accurately track. The gold-standard (human auditors) is insufficient for assessing true overall compliance. Computer vision technology has the ability to perform more accurate appraisals. Our primary objective was to evaluate if a computer vision algorithm could accurately observe hand hygiene dispenser use in images captured by depth sensors. MATERIALS AND METHODS: Sixteen depth sensors were installed on one hospital unit. Images were collected continuously from March to August 2017. Utilizing a convolutional neural network, a machine learning algorithm was trained to detect hand hygiene dispenser use in the images. The algorithm's accuracy was then compared with simultaneous in-person observations of hand hygiene dispenser usage. Concordance rate between human observation and algorithm's assessment was calculated. Ground truth was established by blinded annotation of the entire image set. Sensitivity and specificity were calculated for both human and machine-level observation. RESULTS: A concordance rate of 96.8% was observed between human and algorithm (kappa = 0.85). Concordance among the 3 independent auditors to establish ground truth was 95.4% (Fleiss's kappa = 0.87). Sensitivity and specificity of the machine learning algorithm were 92.1% and 98.3%, respectively. Human observations showed sensitivity and specificity of 85.2% and 99.4%, respectively. CONCLUSIONS: A computer vision algorithm was equivalent to human observation in detecting hand hygiene dispenser use. Computer vision monitoring has the potential to provide a more complete appraisal of hand hygiene activity in hospitals than the current gold-standard given its ability for continuous coverage of a unit in space and time.

An Improvement Effort to Optimize Electronically Generated Hospital Discharge Instructions.

OBJECTIVES: The purpose of hospital discharge instructions (HDIs) is to facilitate safe patient transitions home, but electronic health records can generate lengthy documents filled with irrelevant information. When our institution changed electronic health records, a cumbersome electronic discharge workflow produced low-value HDI and contributed to a spike in discharge delays. Our aim was to decrease these delays while improving family and provider satisfaction with HDI. METHODS: We used quality improvement methodology to redesign the electronic discharge navigator and HDI to address the following issues: (1) difficulty preparing discharge instructions before time of discharge, (2) suboptimal formatting of HDI, (3) lack of standard templates and language within HDI, and (4) difficulties translating HDI into non-English languages. Discharge delays due to HDI were tracked before and after the launch of our new discharge workflow. Parents and providers evaluated HDI and the electronic discharge workflow, respectively, before and after our intervention. Providers audited HDI for content. RESULTS: Discharge delays due to HDI errors decreased from a mean of 3.4 to 0.5 per month after our intervention. Parents' ratings of how understandable our HDIs were improved from 2.35 to 2.74 postintervention (P = .05). Pediatric resident agreement that the electronic discharge process was easy to use increased from 9% to 67% after the intervention (P < .001). CONCLUSIONS: Through multidisciplinary collaboration we facilitated advance preparation of more standardized HDI and decreased related discharge delays from the acute care units at a large tertiary care hospital.

Improving Patient Flow: Analysis of an Initiative to Improve Early Discharge.

BACKGROUND: Discharge delays adversely affect hospital bed availability and thus patient flow. OBJECTIVE: We aimed to increase the percentage of early discharges (EDCs; before 11 am). We hypothesized that obtaining at least 25% EDCs would decrease emergency department (ED) and postanesthesia care unit (PACU) hospital bed wait times. DESIGN: This study used a pre/postintervention retrospective analysis. SETTING: All acute care units in a quaternary care academic children's hospital were included in this study. PATIENTS: The patient sample included all discharges from the acute care units and all hospital admissions from the ED and PACU from January 1, 2014, to December 31, 2016. INTERVENTION: A multidisciplinary team identified EDC barriers, including poor identification of EDC candidates, accountability issues, and lack of team incentives. A total of three successive interventions were implemented using Plan-Do-Check-Act (PDCA) cycles over 10 months between 2015 and 2016 addressing these barriers. Interventions included EDC identification and communication, early rounding on EDCs, and modest incentives. MEASUREMENTS: Calendar month EDC percentage, ED (from time bed requested to the time patient left ED) and PACU (from time patient ready to leave to time patient left PACU) wait times were measured. RESULTS: EDCs increased from an average 8.8% before the start of interventions (May 2015) to 15.8% after interventions (February 2016). Using an interrupted time series, both the jump and the slope increase were significant (3.9%, P = .02 and 0.48%, P < .01, respectively). Wait times decreased from a median of 221 to 133 minutes (P < .001) for ED and from 56 to 36 minutes per patient (P = .002) for PACU. CONCLUSION: A multimodal intervention was associated with more EDCs and decreased PACU and ED bed wait times.

Systems Delivery Innovation for Alzheimer Disease.

OBJECTIVE: The authors describe a comprehensive care model for Alzheimer disease (AD) that improves value within 1-3 years after implementation by leveraging targeted outpatient chronic care management, cognitively protective acute care, and timely caregiver support. METHODS: Using current best evidence, expert opinion, and macroeconomic modeling, the authors designed a comprehensive care model for AD that improves the quality of care while reducing total per capita healthcare spending by more than 15%. Cost savings were measured as reduced spending by payers. Cost estimates were derived from medical literature and national databases, including both public and private U.S. payers. All estimates reflect the value in 2015 dollars using a consumer price index inflation calculator. Outcome estimates were determined at year 2, accounting for implementation and steady-state intervention costs. RESULTS: After accounting for implementation and recurring operating costs of approximately $9.5 billion, estimated net cost savings of between $13 and $41 billion can be accomplished concurrently with improvements in quality and experience of coordinated chronic care ($0.01-$6.8 billion), cognitively protective acute care ($8.7-$26.6 billion), timely caregiver support ($4.3-$7.5 billion), and caregiver efficiency ($4.1-$7.2 billion). CONCLUSION: A high-value care model for AD may improve the experience of patients with AD while significantly lowering costs.

Improving Communication with Primary Care Physicians at the Time of Hospital Discharge.

BACKGROUND: Communication with primary care physicians (PCPs) at the time of a patient's hospital discharge is important to safely transition care to home. The goal of this quality improvement initiative was to increase discharge communication to PCPs at an academic children's hospital. METHODS: A multidisciplinary team at Lucile Packard Children's Hospital Stanford used Lean A3 problem solving methodology to address the problem of inadequate discharge communication with PCPs. Emphasis was placed on frontline provider (resident physicians) involvement in the improvement process, creating standards, and error proofing. Root cause analysis identified several key drivers of the problem, and successive countermeasures were implemented beginning in August 2013 aimed at achieving the target of 80% attempted verbal communication within seven days before or after (usually 24-48 hours) on the pediatric medical services. Run charts were generated tracking the outcome of PCP communication. RESULTS: On the pediatric medical services, the goal of 80% communication was met and sustained during a seven-month period starting October 2013, a statistically significant improvement. In the eight months prior to October 2013, hospitalwide PCP communication prior to discharge averaged 59.1% (n = 5,397) and improved to 76.7% (n = 4,870) in the seven months after (p <0.001). Fifteen of 19 specialty services had a significant increase in discharge communication after October 2013. CONCLUSION: Lean improvement methodology (including structured problem solving using A3 thinking), intensive frontline provider involvement, and process-oriented electronic health record work flow redesign led to increased verbal PCP communication at around the time of a patient's discharge.

Opportunities to improve the value of outpatient surgical care.

OBJECTIVES: Nearly 57 million outpatient surgeries-invasive procedures performed on an outpatient basis in hospital outpatient departments (HOPDs) or ambulatory surgery centers (ASCs)-produced annually in the United States account for roughly 7% of healthcare expenditures. Although moving inpatient surgeries to outpatient settings has lowered the cost of care, substantial opportunities to improve the value of outpatient surgery remain. To exploit these remaining opportunities, we composed an evidence-based care delivery composite for national discussion and pilot testing. STUDY DESIGN: Evidence-based care delivery composite. METHODS: We synthesized peer-reviewed publications describing efforts to improve the value of outpatient surgical care, interviewed patients and clinicians to understand their most deeply felt discontents, reviewed potentially relevant emerging science and technology, and observed surgeries at healthcare organizations nominated by researchers as exemplars of efficiency and effectiveness. Primed by this information, we iterated potential new designs utilizing criticism from practicing clinicians, health services researchers, and healthcare managers. RESULTS: We found that 3 opportunities are most likely to improve value: 1) maximizing the appropriate use of surgeries via decision aids, clinical decision support, and a remote surgical coach for physicians considering a surgical referral; 2) safely shifting surgeries from HOPDs to high-volume, multi-specialty ASCs where costs are much lower; and 3) standardizing processes in ASCs from referral to recovery. CONCLUSIONS: Extrapolation based on published studies of the effects of each component suggests that the proposed 3-part composite may lower annual national outpatient surgical spending by as much as one-fifth, while maintaining or improving outcomes and the care experience for patients and clinicians. Pilot testing and evaluation will allow refinement of this composite.

Safety analysis of proposed data-driven physiologic alarm parameters for hospitalized children.

INTRODUCTION: Modification of alarm limits is one approach to mitigating alarm fatigue. We aimed to create and validate heart rate (HR) and respiratory rate (RR) percentiles for hospitalized children, and analyze the safety of replacing current vital sign reference ranges with proposed data-driven, age-stratified 5th and 95th percentile values. METHODS: In this retrospective cross-sectional study, nurse-charted HR and RR data from a training set of 7202 hospitalized children were used to develop percentile tables. We compared 5th and 95th percentile values with currently accepted reference ranges in a validation set of 2287 patients. We analyzed 148 rapid response team (RRT) and cardiorespiratory arrest (CRA) events over a 12-month period, using HR and RR values in the 12 hours prior to the event, to determine the proportion of patients with out-of-range vitals based upon reference versus data-driven limits. RESULTS: There were 24,045 (55.6%) fewer out-of-range measurements using data-driven vital sign limits. Overall, 144/148 RRT and CRA patients had out-of-range HR or RR values preceding the event using current limits, and 138/148 were abnormal using data-driven limits. Chart review of RRT and CRA patients with abnormal HR and RR per current limits considered normal by data-driven limits revealed that clinical status change was identified by other vital sign abnormalities or clinical context. CONCLUSIONS: A large proportion of vital signs in hospitalized children are outside presently used norms. Safety evaluation of data-driven limits suggests they are as safe as those currently used. Implementation of these parameters in physiologic monitors may mitigate alarm fatigue. Journal of Hospital Medicine 2015;11:817-823. © 2015 Society of Hospital Medicine.

Better health, less spending: Redesigning the transition from pediatric to adult healthcare for youth with chronic illness.

Adolescents and young adults (AYA) with serious chronic illnesses face costly and dangerous gaps in care as they transition from pediatric to adult health systems. New, financially sustainable approaches to transition are needed to close these gaps. We designed a new transition model for adolescents and young adults with a variety of serious chronic conditions. Our explicit goal was to build a model that would improve the value of care for youth 15-25 years of age undergoing this transition. The design process incorporated a review, analysis, and synthesis of relevant clinical and health services research; stakeholder interviews; and observations of high-performing healthcare systems. We identified three major categories of solutions for a safer and lower cost transition to adult care: (1) building and supporting self-management during the critical transition; (2) engaging receiving care; and (3) providing checklist-driven guide services during the transition. We propose that implementation of a program with these interventions would have a positive impact on all three domains of the triple aim - improving health, improving the experience of care, and reducing per capita healthcare cost. The transition model provides a general framework as well as suggestions for specific interventions. Pilot tests to assess the model's ease of implementation, clinical effects, and financial impact are currently underway.

Implementation of Data Drive Heart Rate and Respiratory Rate parameters on a Pediatric Acute Care Unit.

The majority of hospital physiologic monitor alarms are not clinically actionable and contribute to alarm fatigue. In 2014, The Joint Commission declared alarm safety as a National Patient Safety Goal and urged prompt action by hospitals to mitigate the issue [1]. It has been demonstrated that vital signs in hospitalized children are quite different from currently accepted reference ranges [2]. Implementation of data-driven, age stratified vital sign parameters (Table 1) for alarms in this patient population could reduce alarm frequency.