Thesis (Ph. D.)--Virginia Commonwealth University, 2007. / Prepared for: Dept. of Health Administration. Bibliography: leaves 175-186.

Through significant federal investment and incentives, Electronic Health Records have become ubiquitous in modern hospitals. Over the past decade, these computer support systems have provided healthcare operations with new safety nets, and efficiency increases, but also introduce new problems when they suddenly go offline. These downtime events are chaotic and dangerous for patients. With the safety systems clinicians have become accustomed to offline, patients are at risk from errors and delays. This work applies the Macroergonomic methodology to facilitate an exploratory study into the issues related to patient care during downtime events. This work uses data from existing sources within the hospital, such as the electronic health record itself. Data collection mechanisms included interviews, downtime paper reviews, and workplace observations. The triangulation of data collection mechanisms facilitated a thorough exploration of the issues of downtime. The Macroergonomic Analysis and Design (MEAD) methodology was used to guide the analysis of the data, and identify variances and shifts in responsibility due to downtime. The analysis of the data supports and informs developing potential intervention strategies to enable hospitals to better cope with downtime events. Within MEAD, the assembled data is used to inform the creation of a simulation model which was used to test the efficacy of the intervention strategies. The results of the simulation testing are used to determine the specific parameters of the intervention suggestions as they relate to the target hospitals. The primary contributions of this work are an exploratory study of electronic health record downtime and impacts to patient safety, and an adaptation of the Macroergonomic Analysis and Design methodology, employing multiple data collection methods and a high-fidelity simulation model. The methodology is intended to guide future research into the downtime issue, and the direct findings can inform the creation of better downtime contingency strategies for the target hospitals, and possibly to offer some generalizability for all hospitals. / Ph. D. / Hospitals experience periodic outages of their computerized work support systems from a variety of causes. These outages can range from partial communication and or access restrictions to total shutdown of all computer systems. Hospitals operating during a computerized outage or downtime are potentially unable to access computerized records, procedures and conduct patient care activities which are facilitated by computerized systems. Hospitals are in need of a means to cope with the complications of downtime and the loss of computerized support systems without risking patient care. This dissertation assesses downtime preparedness and planning through the application of Macroergonomics which has incorporated discrete event simulation. The results provide a further understanding of downtime risks and deficiencies in current planning approaches. The study enhances the application of Macroergonomics and demonstrates the value of discrete event simulation as a tool to aid in Macroergonomic evaluations. Based on the Macroergonomic Analysis and Design method, downtime improvement strategies are developed and tested, demonstrating their potential efficacy over baseline. Through this dissertation, the deficiencies in current contingency plans are examined and exposed and further the application of Macroergonomics in healthcare.

Healthcare-associated infections (HAIs) are used as a measure for federal value-based payment programs. Using data for 2015, the Centers for Disease Control and Prevention (CDC) developed newer risk adjustment models to calculate the standardized infection ratio (SIR) for various infections occurring in hospitals. New national baselines were set to compare performance among medical facilities and states. Despite adjustments for various facility-level factors that contribute to HAI risk, there are ongoing concerns that SIR calculations do not adequately account for non-hospital risk factors that have been linked to clinical outcomes. This explanatory study evaluates state-level data using simple linear regression to determine relationships between the standardized infection ratio (SIR) for methicillin-resistant Staphylococcus aureus (MRSA) bacteremia and several socioeconomic and geographic factors. Bivariate analysis produced significant correlation between SIR and high school education, with states exhibiting lower SIR relative to the percent of adults who completed high school. Higher SIRs were found relative to the percent of state populations subjected to poverty, obesity, and diagnosis of diabetes. Percent of nonprofit hospitals, adults with bachelor’s degrees, and rural residents were not significantly correlated with state measures of MRSA bacteremia. These findings can help guide efforts to reduce HAIs, improve safety of care, and advance population health efforts. The results from this study reinforce the notion that non-hospital factors may have significant effects on the incidence of MRSA bacteremia events occurring in hospitalized patients. Current risk adjustment models that predict MRSA bacteremia events for quality reporting purposes may not adequately account for these risk factors. The present study highlights some ways that hospitals, patients, and policymakers can work together to address social risk factors as a strategy for promoting better and safer care, and healthier communities. This study investigates aspects of the bigger picture of health care quality, performance measurement, and population health. This “feel for the whole” underscores the implications on state performance in infection prevention in the context of socioeconomic and medical vulnerabilities. The study emphasizes the need for greater multidisciplinary collaboration to address community health needs and reduce social and medical disparities.