Relationship Between In-Hospital Adverse Events and Hospital Performance on 30-Day All-cause Mortality and Readmission for Patients With Heart Failure.

BACKGROUND: Hospitals with high mortality and readmission rates for patients with heart failure (HF) might also perform poorly in other quality concepts. We sought to evaluate the association between hospital performance on mortality and readmission with hospital performance rates of safety adverse events. METHODS: This cross-sectional study linked the 2009 to 2019 patient-level adverse events data from the Medicare Patient Safety Monitoring System, a randomly selected medical records-abstracted patient safety database, to the 2005 to 2016 hospital-level HF-specific 30-day all-cause mortality and readmissions data from the United States Centers for Medicare & Medicaid Services. Hospitals were classified to one of 3 performance categories based on their risk-standardized 30-day all-cause mortality and readmission rates: better (both in <25th percentile), worse (both >75th percentile), and average (otherwise). Our main outcome was the occurrence (yes/no) of one or more adverse events during hospitalization. A mixed-effect model was fit to assess the relationship between a patient's risk of having adverse events and hospital performance categories, adjusted for patient and hospital characteristics. RESULTS: The study included 39 597 patients with HF from 3108 hospitals, of which 252 hospitals (8.1%) and 215 (6.9%) were in the better and worse categories, respectively. The rate of patients with one or more adverse events during a hospitalization was 12.5% (95% CI, 12.1-12.8). Compared with patients admitted to better hospitals, patients admitted to worse hospitals had a higher risk of one or more hospital-acquired adverse events (adjusted risk ratio, 1.24 [95% CI, 1.06-1.44]). CONCLUSIONS: Patients admitted with HF to hospitals with high 30-day all-cause mortality and readmission rates had a higher risk of in-hospital adverse events. There may be common quality issues among these 3 measure concepts in these hospitals that produce poor performance for patients with HF.

Trends in Adverse Event Rates in Hospitalized Patients, 2010-2019.

Importance: Patient safety is a US national priority, yet lacks a comprehensive assessment of progress over the past decade. Objective: To determine the change in the rate of adverse events in hospitalized patients. Design, Setting, and Participants: This serial cross-sectional study used data from the Medicare Patient Safety Monitoring System from 2010 to 2019 to assess in-hospital adverse events in patients. The study included 244 542 adult patients hospitalized in 3156 US acute care hospitals across 4 condition groups from 2010 through 2019: acute myocardial infarction (17%), heart failure (17%), pneumonia (21%), and major surgical procedures (22%); and patients hospitalized from 2012 through 2019 for all other conditions (22%). Exposures: Adults aged 18 years or older hospitalized during each included calendar year. Main Outcomes and Measures: Information on adverse events (abstracted from medical records) included 21 measures across 4 adverse event domains: adverse drug events, hospital-acquired infections, adverse events after a procedure, and general adverse events (hospital-acquired pressure ulcers and falls). The outcomes were the total change over time for the observed and risk-adjusted adverse event rates in the subpopulations. Results: The study sample included 190 286 hospital discharges combined in the 4 condition-based groups of acute myocardial infarction, heart failure, pneumonia, and major surgical procedures (mean age, 68.0 [SD, 15.9] years; 52.6% were female) and 54 256 hospital discharges for the group including all other conditions (mean age, 57.7 [SD, 20.7] years; 59.8% were female) from 3156 acute care hospitals across the US. From 2010 to 2019, the total change was from 218 to 139 adverse events per 1000 discharges for acute myocardial infarction, from 168 to 116 adverse events per 1000 discharges for heart failure, from 195 to 119 adverse events per 1000 discharges for pneumonia, and from 204 to 130 adverse events per 1000 discharges for major surgical procedures. From 2012 to 2019, the rate of adverse events for all other conditions remained unchanged at 70 adverse events per 1000 discharges. After adjustment for patient and hospital characteristics, the annual change represented by relative risk in all adverse events per 1000 discharges was 0.94 (95% CI, 0.93-0.94) for acute myocardial infarction, 0.95 (95% CI, 0.94-0.96) for heart failure, 0.94 (95% CI, 0.93-0.95) for pneumonia, 0.93 (95% CI, 0.92-0.94) for major surgical procedures, and 0.97 (95% CI, 0.96-0.99) for all other conditions. The risk-adjusted adverse event rates declined significantly in all patient groups for adverse drug events, hospital-acquired infections, and general adverse events. For patients in the major surgical procedures group, the risk-adjusted rates of events after a procedure declined significantly. Conclusions and Relevance: In the US between 2010 and 2019, there was a significant decrease in the rates of adverse events abstracted from medical records for patients admitted for acute myocardial infarction, heart failure, pneumonia, and major surgical procedures and there was a significant decrease in the adjusted rates of adverse events between 2012 and 2019 for all other conditions. Further research is needed to understand the extent to which these trends represent a change in patient safety.

Dashboard Design to Identify and Balance Competing Risk of Multiple Hospital-Acquired Conditions.

BACKGROUND: Hospital-acquired conditions (HACs) are common, costly, and national patient safety priority. Catheter-associated urinary tract infections (CAUTIs), hospital-acquired pressure injury (HAPI), and falls are common HACs. Clinicians assess each HAC risk independent of other conditions. Prevention strategies often focus on the reduction of a single HAC rather than considering how actions to prevent one condition could have unintended consequences for another HAC. OBJECTIVES: The objective of this study is to design an empirical framework to identify, assess, and quantify the risks of multiple HACs (MHACs) related to competing single-HAC interventions. METHODS: This study was an Institutional Review Board approved, and the proof of concept study evaluated MHAC Competing Risk Dashboard to enhance clinicians' management combining the risks of CAUTI, HAPI, and falls. The empirical model informing this study focused on the removal of an indwelling urinary catheter to reduce CAUTI, which may impact HAPI and falls. A multisite database was developed to understand and quantify competing risks of HACs; a predictive model dashboard was designed and clinical utility of a high-fidelity dashboard was qualitatively tested. Five hospital systems provided data for the predictive model prototype; three served as sites for testing and feedback on the dashboard design and usefulness. The participatory study design involved think-aloud methods as the clinician explored the dashboard. Individual interviews provided an understanding of clinician's perspective regarding ease of use and utility. RESULTS: Twenty-five clinicians were interviewed. Clinicians favored a dashboard gauge design composed of green, yellow, and red segments to depict MHAC risk associated with the removal of an indwelling urinary catheter to reduce CAUTI and possible adverse effects on HAPI and falls. CONCLUSION: Participants endorsed the utility of a visual dashboard guiding clinical decisions for MHAC risks preferring common stoplight color understanding. Clinicians did not want mandatory alerts for tool integration into the electronic health record. More research is needed to understand MHAC and tools to guide clinician decisions.

Analysis of Hospital-Level Readmission Rates and Variation in Adverse Events Among Patients With Pneumonia in the United States.

Importance: It is known that hospitalized patients who experience adverse events are at greater risk of readmission; however, it is unknown whether patients admitted to hospitals with higher risk-standardized readmission rates had a higher risk of in-hospital adverse events. Objective: To evaluate whether patients with pneumonia admitted to hospitals with higher risk-standardized readmission rates had a higher risk of adverse events. Design, Setting, and Participants: This cross-sectional study linked patient-level adverse events data from the Medicare Patient Safety Monitoring System (MPSMS), a randomly selected medical record abstracted database, to the hospital-level pneumonia-specific all-cause readmissions data from the Centers for Medicare & Medicaid Services. Patients with pneumonia discharged from July 1, 2010, through December 31, 2019, in the MPSMS data were included. Hospital performance on readmissions was determined by the risk-standardized 30-day all-cause readmission rate. Mixed-effects models were used to examine the association between adverse events and hospital performance on readmissions, adjusted for patient and hospital characteristics. Analysis was completed from October 2019 through July 2020 for data from 2010 to 2017 and from March through April 2022 for data from 2018 to 2019. Exposures: Patients hospitalized for pneumonia. Main Outcomes and Measures: Adverse events were measured by the rate of occurrence of hospital-acquired events and the number of events per 1000 discharges. Results: The sample included 46 047 patients with pneumonia, with a median (IQR) age of 71 (58-82) years, with 23 943 (52.0%) women, 5305 (11.5%) Black individuals, 37 763 (82.0%) White individuals, and 2979 (6.5%) individuals identifying as another race, across 2590 hospitals. The median hospital-specific risk-standardized readmission rate was 17.0% (95% CI, 16.3%-17.7%), the occurrence rate of adverse events was 2.6% (95% CI, 2.54%-2.65%), and the number of adverse events per 1000 discharges was 157.3 (95% CI, 152.3-162.5). An increase by 1 IQR in the readmission rate was associated with a relative 13% higher patient risk of adverse events (adjusted odds ratio, 1.13; 95% CI, 1.08-1.17) and 5.0 (95% CI, 2.8-7.2) more adverse events per 1000 discharges at the patient and hospital levels, respectively. Conclusions and Relevance: Patients with pneumonia admitted to hospitals with high all-cause readmission rates were more likely to develop adverse events during the index hospitalization. This finding strengthens the evidence that readmission rates reflect the quality of hospital care for pneumonia.

Pursuing Patient Safety at the Intersection of Design, Systems Engineering, and Health Care Delivery Research: An Ongoing Assessment.

OBJECTIVES: Despite endorsements for greater use of systems approaches and reports from national consensus bodies calling for closer engineering/health care partnerships to improve care delivery, there has been a scarcity of effort of actually engaging the design and engineering disciplines in patient safety projects. The article describes a grant initiative undertaken by the Agency for of Healthcare Research and Quality that brings these disciplines together to test new ideas that could make health care safer. METHODS: Collectively known as patient safety learning laboratories, grantee teams engage in phase-based activities that parallel a systems engineering process-problem analysis, design, development, implementation, and evaluation-to gain an in-depth understanding of related patient safety problems, generate fresh ideas and rapid prototypes, develop the prototypes, ensure that developed components are implemented as an integrated working system, and evaluate the system in a simulated or clinical setting. FINDINGS: Obstacles are described that can derail the best of intentions in deploying the systems engineering methodology. Based on feedback received from project teams, lessons learned are emerging that find considerable variation among project teams in deploying the methodology and a longer than anticipated amount of time in bringing team members from different disciplines together where they learn to communicate and function as a team. CONCLUSIONS: Three narratives are generated in terms of what success might look like. Much is yet to be learned about the limitations and successes of the ongoing learning laboratory initiative, which should be relevant to the broader scale interest in learning health systems.

Design and Evaluation of Low-Fidelity Visual Display Prototypes for Multiple Hospital-Acquired Conditions.

Hospital-acquired conditions such as catheter-associated urinary tract infection, stage 3 or 4 hospital-acquired pressure injury, and falls with injury are common, costly, and largely preventable. This study used participatory design methods to design and evaluate low-fidelity prototypes of clinical dashboards to inform high-fidelity prototype designs to visualize integrated risks based on patient profiles. Five low-fidelity prototypes were developed through literature review and by engaging nurses, nurse managers, and providers as participants (N = 23) from two hospitals in different healthcare systems using focus groups and interviews. Five themes were identified from participatory design sessions: Need for Integrated Hospital-Acquired Condition Risk Tool, Information Needs, Sources of Information, Trustworthiness of Information, and Performance Tracking Perspectives. Participants preferred visual displays that represented patient comparative risks for hospital-acquired conditions using the familiar design metaphor of a gauge and green, yellow, and red "traffic light" colors scheme. Findings from this study were used to design a high-fidelity prototype to be tested in the next phase of the project. Visual displays of hospital-acquired conditions that are familiar in display and simplify complex information such as the green, yellow, and red dashboard are needed to assist clinicians in fast-paced clinical environments and be designed to prevent alert fatigue.

Association Between Medicare Expenditures and Adverse Events for Patients With Acute Myocardial Infarction, Heart Failure, or Pneumonia in the United States.

Importance: Studies have shown that adverse events are associated with increasing inpatient care expenditures, but contemporary data on the association between expenditures and adverse events beyond inpatient care are limited. Objective: To evaluate whether hospital-specific adverse event rates are associated with hospital-specific risk-standardized 30-day episode-of-care Medicare expenditures for fee-for-service patients discharged with acute myocardial infarction (AMI), heart failure (HF), or pneumonia. Design, Setting, and Participants: This cross-sectional study used the 2011 to 2016 hospital-specific risk-standardized 30-day episode-of-care expenditure data from the Centers for Medicare & Medicaid Services and medical record-abstracted in-hospital adverse event data from the Medicare Patient Safety Monitoring System. The setting was acute care hospitals treating at least 25 Medicare fee-for-service patients for AMI, HF, or pneumonia in the United States. Participants were Medicare fee-for-service patients 65 years or older hospitalized for AMI, HF, or pneumonia included in the Medicare Patient Safety Monitoring System in 2011 to 2016. The dates of analysis were July 16, 2017, to May 21, 2018. Main Outcomes and Measures: Hospitals' risk-standardized 30-day episode-of-care expenditures and the rate of occurrence of adverse events for which patients were at risk. Results: The final study sample from 2194 unique hospitals included 44 807 patients (26.1% AMI, 35.6% HF, and 38.3% pneumonia) with a mean (SD) age of 79.4 (8.6) years, and 52.0% were women. The patients represented 84 766 exposures for AMI, 96 917 exposures for HF, and 109 641 exposures for pneumonia. Patient characteristics varied by condition but not by expenditure category. The mean (SD) risk-standardized expenditures were $22 985 ($1579) for AMI, $16 020 ($1416) for HF, and $16 355 ($1995) for pneumonia per hospitalization. The mean risk-standardized rates of occurrence of adverse events for which patients were at risk were 3.5% (95% CI, 3.4%-3.6%) for AMI, 2.5% (95% CI, 2.5%-2.5%) for HF, and 3.0% (95% CI, 2.9%-3.0%) for pneumonia. An increase by 1 percentage point in the rate of occurrence of adverse events was associated with an increase in risk-standardized expenditures of $103 (95% CI, $57-$150) for AMI, $100 (95% CI, $29-$172) for HF, and $152 (95% CI, $73-$232) for pneumonia per discharge. Conclusions and Relevance: Hospitals with high adverse event rates were more likely to have high 30-day episode-of-care Medicare expenditures for patients discharged with AMI, HF, or pneumonia.

Challenges in Health Care Simulation: Are We Learning Anything New?

The growth of health care simulation in schools of medicine and nursing is noteworthy, as is the increasingly sophisticated simulation technology, support from funding agencies and foundations for research, well-attended annual conferences, and continued interest of accreditation and certification groups. Yet there are concerns preventing the full value of health care simulation to be realized when examined from a patient safety perspective. Basic questions are asked by funders of patient safety research when assessing past simulation projects undertaken to advance patient safety: Are the safety and quality of care to patients actually improved, and is something new being learned regarding the optimal use of simulation? Concerns focus on pursuing the right research questions to learn something new about the most effective use of simulation; doing more with simulation than simply providing an interesting, stand-alone educational experience; attending more seriously to how skill acquisition, maintenance, and progression get managed; and encouraging investigators, funders, and reviewers to expand their vision regarding what constitutes important inquiry and evidence in health care simulation. Patient safety remains a multifaceted challenge in the United States, requiring multifaceted approaches. Simulation training is considered a promising approach for improving the safety and quality of health services delivery. While it takes time for any new approach to gain momentum and learn from past efforts, it also will require addressing a systematic range of essential questions to improve existing knowledge on the optimal use of simulation, and to realize similar gains in safety that other high-risk industries have made.

Nonlinear behavior of the center of pressure in simulated standing on elevated construction beams.

This study investigated the effects of width of construction beams and single-hand load holding task conditions on nonlinear behavior of the foot center of pressure (COP) exerted on the beam. The foot COP, defined as the point of application of the result of vertical forces acting on the surface of foot support, was measured in the lateral direction under simulated standing task conditions. Twelve healthy male subjects were asked to hold a load of 6.8 kg and 11.3 kg while standing on the elevated construction beams with widths of 10 and 22.5 cm (4 and 9 inches, respectively) under low and high foot separation (foot step). The results showed that both beam width and single-hand load carrying conditions had significant effects on the observed nonlinearity of the foot center of pressure exerted on the beam. Standing on the narrow beam resulted in higher level of chaotic behavior of COP compared to the wide beam condition. The nonlinearity of the COP exerted by the forward (left) foot was higher for the narrow beam condition. For both beams, the nonlinearity of the COP exerted by the forward (left) foot was consistently higher than the COP exerted by the backward (right) foot. Furthermore, for both beams, single-handed holding of the 11.3 kg load resulted in higher levels of COP nonlinearity than carrying 6.8 kg or no load at all. The study results indicate that nonlinear dynamics behavior of the forward foot under single-handed high load holding condition may be critical to preserving lateral stability during standing at the construction beams.

Nonlinear dynamical behavior of surface electromyographical signals of biceps muscle under two simulated static work postures.

The primary objective of this study was to explore nonlinear characteristics, if any, of the surface myoelectrical (EMG) activity of the biceps muscle during simulated maximum voluntary contraction at a reference arm posture (MVCRP), and at an elevated static arm work posture (EAWP) observed on the manufacturing assembly line. The results showed that positive Lyapunov exponents existed for each of the trials in both work postures. The statistical test of significance showed that surface EMG of the biceps brachii was more chaotic under the MVCRP condition as compared to the EWAP condition. Based on the Kaplan-Yorke dimension, it was observed that the recorded two types of time series EMG data were similar in complexity. The study also found a significant difference in fractal dimensions between the two postures. It was concluded that the nonlinear dynamical EMG properties expressed in terms of the Lyapunov exponents, Kaplan-Yorke dimension, and fractal dimensions could be used as sensitive indices for evaluation of the level of muscular efforts under static work conditions.

A neuro-fuzzy model for estimating electromyographical activity of trunk muscles due to manual lifting.

The main objective of this study was to develop a hybrid neuro-fuzzy system for estimating the magnitude of EMG responses of 10 trunk muscles based on two lifting task variables (trunk velocity and trunk moment) as model inputs. The input and output variables were represented using the fuzzy membership functions. The initial fuzzy rules were generated by the neural network using true EMG data. Two different laboratory-derived EMG data sets were used for model development and validation, respectively. The mean absolute error (MAE) between the actual and model-estimated normalized EMG values was calculated. Across all muscles, the average value of MAE was 8.43% (SD=2.87%) of the normalized EMG data. The larger absolute errors occurred in the left side of the trunk, which exhibited higher levels of muscular activity. Overall, the developed model was capable of estimating the normalized EMG values with average value of the mean absolute differences of 6.4%. It was hypothesized that model performance could be improved by increasing the number of inputs, including additional task variables as well as the subjects' characteristics.