A quality improvement project to improve the Medicare and Medicaid Services (CMS) sepsis bundle compliance rate in a large healthcare system.

Sepsis is a leading cause of mortality and morbidity in hospitalised patients. The Centers for Medicare and Medicaid Services (CMS) mandated that US hospitals report sepsis bundle compliance rate as a quality process measure in October 2015. The specific aim of our study was to improve the CMS sepsis bundle compliance rate from 30% to 40% across 20 acute care hospitals in our healthcare system within 1 year. The study included all adult inpatients with sepsis sampled according to CMS specifications from October 2015 to September 2016. The CMS sepsis bundle compliance rate was tracked monthly using statistical process control charting. A baseline rate of 28.5% with 99% control limits was established. We implemented multiple interventions including computerised decision support systems (CDSSs) to increase compliance with the most commonly missing bundle elements. Compliance reached 42% (99% statistical process control limits 18.4%-38.6%) as CDSS was implemented system-wide, but this improvement was not sustained after CMS changed specifications of the outcome measure. Difficulties encountered elucidate shortcomings of our study methodology and of the CMS sepsis bundle compliance rate as a quality process measure.

ICU Telemedicine Comanagement Methods and Length of Stay.

BACKGROUND: Studies have identified processes that are associated with more favorable length of stay (LOS) outcomes when an ICU telemedicine program is implemented. Despite these studies, the relation of the acceptance of ICU telemedicine management services by individual ICUs to LOS outcomes is unknown. METHODS: This is a single ICU telemedicine center study that compares LOS outcomes among three groups of intensivist-staffed mixed medical-surgical ICUs that used alternative comanagement strategies. The proportion of provider orders recorded by an ICU telemedicine provider to all recorded orders was compared among ICUs that used a monitor and notify comanagement approach, a direct intervention with timely notification process, and ICUs that used a mix of these two approaches. The primary outcome was acuity-adjusted hospital LOS. RESULTS: ICUs that used the direct intervention with timely notification strategy had a significantly larger proportion of provider orders recorded by ICU telemedicine physicians than the mixed methods of comanagement group, which had a larger proportion than ICUs that used the monitor and notify method (P < .001). Acuity-adjusted hospital LOS was significantly lower for the direct intervention with timely notification comanagement strategy (0.68; 0.65-0.70) compared with the mixed methods group (0.70 [0.69-0.72]; P = .01), which was significantly lower than the monitor and notify group (0.83 [0.80-0.86]; P < .001). CONCLUSIONS: Direct intervention with timely notification strategies of ICU telemedicine comanagement were associated with shorter LOS outcomes than monitor and notify comanagement strategies.

Real-Time Automated Sampling of Electronic Medical Records Predicts Hospital Mortality.

BACKGROUND: Real-time automated continuous sampling of electronic medical record data may expeditiously identify patients at risk for death and enable prompt life-saving interventions. We hypothesized that a real-time electronic medical record-based alert could identify hospitalized patients at risk for mortality. METHODS: An automated alert was developed and implemented to continuously sample electronic medical record data and trigger when at least 2 of 4 systemic inflammatory response syndrome criteria plus at least one of 14 acute organ dysfunction parameters was detected. The systemic inflammatory response syndrome and organ dysfunction alert was applied in real time to 312,214 patients in 24 hospitals and analyzed in 2 phases: training and validation datasets. RESULTS: In the training phase, 29,317 (18.8%) triggered the alert and 5.2% of such patients died, whereas only 0.2% without the alert died (unadjusted odds ratio 30.1; 95% confidence interval, 26.1-34.5; P < .0001). In the validation phase, the sensitivity, specificity, area under the curve, and positive and negative likelihood ratios for predicting mortality were 0.86, 0.82, 0.84, 4.9, and 0.16, respectively. Multivariate Cox-proportional hazard regression model revealed greater hospital mortality when the alert was triggered (adjusted hazards ratio 4.0; 95% confidence interval, 3.3-4.9; P < .0001). Triggering the alert was associated with additional hospitalization days (+3.0 days) and ventilator days (+1.6 days; P < .0001). CONCLUSION: An automated alert system that continuously samples electronic medical record data can be implemented, has excellent test characteristics, and can assist in the real-time identification of hospitalized patients at risk for death.

Critical care telemedicine: evolution and state of the art.

OBJECTIVES: To review the growth and current penetration of ICU telemedicine programs, association with outcomes, studies of their impact on medical education, associations with medicolegal risks, identify program revenue sources and costs, regulatory aspects, and the ICU telemedicine research agenda. DATA SOURCES: Review of the published medical literature, governmental documents, and opinions of experts from the Society of Critical Care Medicine ICU Telemedicine Committee. DATA SYNTHESIS: Formal ICU telemedicine programs now support 11% of nonfederal hospital critically ill adult patients. There is increasingly robust evidence of association with lower ICU (0.79; 95% CI, 0.65-0.96) and hospital mortality (0.83; 95% CI, 0.73-0.94) and shorter ICU (-0.62 d; 95% CI, -1.21 to -0.04 d) and hospital (-1.26 d; 95% CI, -2.49 to -0.03 d) length of stay. Physicians in training report experiences with telemedicine intensivists that are positive and increased patient safety. Early studies suggest that implementation of ICU telemedicine programs has been associated with lower numbers of malpractice claims and costs. The requirements for Medicare reimbursement and states with legislation addressing providing professional services by telemedicine are detailed. CONCLUSIONS: The inclusion of an ICU telemedicine program as a major part of their critical care delivery paradigm has been implemented for 11% of critically ill U.S. adults as a solution for the problem of access to adult critical care services. Implementation of an ICU telemedicine program is one practical way to increase access and reduce mortality as well as length of stay. ICU telemedicine research including comparative effectiveness studies is urgently needed.

Intensive care telemedicine: evaluating a model for proactive remote monitoring and intervention in the critical care setting.

Historically, telemedicine has focused on the application of traditional physician-to-patient (and physician-to-physician) interactions enhanced by two-way video and audio capability. This "one-on-one" interaction via a telemedicine link can dramatically extend a physician's or other caregiver's geographic range and availability. However, this same telemedicine model is most often implemented "on-demand" for a specified time-limited encounter. The remote Intensive Care Unit (ICU) model to be described similarly expands the geographic range of ICU physicians, but also allows a single specialist to simultaneously monitor multiple patients on a continuous basis by leveraging computerized "intelligent" algorithms and an electronic medical record interface. This new application of telemedicine wedded to computer technology facilitates maximum leveraging of specialists' cognitive skills but also mandates significant process changes in how ICU services are provided. In short, the remote ICU represents a "re-engineering" of how ICU care is delivered and establishes a new paradigm for the field of telemedicine, expanding the reach, scope and availability of intensivist specialty expertise.The re-engineering occurs through a number of ways. First, the telemedicine connection is continuously available in a pro-active fashion that can be provided 24 hours a day, 7 days a week (24/7). Secondly, the system utilizes computerized clinical intelligence algorithms with direct electronic links to physiologic, laboratory and lab/pharmacy data as well as patient diagnoses to focus attention on potential adverse outcomes or trends in individual patients and notify caregivers before trends manifest as adverse outcomes. Third, the traditional physician, nurse, and patient relationship is substantially augmented when there is an ICU physician immediately available to address issues in patient care, particularly at night when physicians are less likely to be present at the bedside. The current preliminary data suggest that this system can be quite effective in improving ICU quality of care, thus leading to reductions in the cost of ICU care, ICU patient mortality, ICU patient outliers, and ICU length of stay (LOS). Given the extensive data showing improved ICU outcomes with daily ICU physician participation in care of critically ill patients, and the national shortage of ICU physicians, nurses, and ancillary staff; the electronic ICU system is gaining popularity as an alternative paradigm for the expansion of an ICU team's expertise in the care of the severely ill. Interestingly, internal Quality Improvement (QI) data from several healthcare systems have shown that improved outcomes occur even when remote ICU telemedicine is applied to a pre-existing 24/7 in-house intensivist care model. The reasons for this remain speculative at this point, but pro-active and hourly remote "virtual rounds" on the most critically-ill patients, and use of computerized algorithms in triaging ICU physicians' attention may contribute to the success of this system. Also, we will show how the system supports key elements of error reduction theory even in well-staffed critical care units. Multiple challenges remain before remote ICU systems become more broadly accepted and applied. These include cost of implementation of the system, resistance to the system by ICU physicians and nurses, and integration of data systems and clinical information into the remote electronic ICU model. In this chapter, we will provide background information on error reduction theory and the role of the remote ICU model, review current data supporting use of the remote ICU system, address the current obstacles to effective implementation, and look to the future of the field for solutions to these challenges.