A Model to Create an Efficient and Equitable Admission Policy for Patients Arriving to the Cardiothoracic ICU

To develop queuing and simulation-based models to understand the relationship between ICU bed availability and operating room schedule to maximize the use of critical care resources and minimize case cancellation while providing equity to patients and surgeons. Queuing theory and computer simulation can be used to model case flow through a cardiothoracic operating room and ICU. A dynamic admission policy that looks at current waiting time and expected ICU length of stay allows for increased equity between patients with only minimum losses of efficiency. This dynamic admission policy would seem to be a superior in maximizing case-flow. These results may be generalized to other surgical ICUs

Low Risk Monitoring in Neurocritical Care

Background/Rationale: Patients are admitted to Intensive care units (ICUs) either because they need close monitoring despite a low risk of hospital mortality (LRM group) or to receive ICU specific active treatments (AT group). The characteristics and differential outcomes of LRM patients vs. AT patients in Neurocritical Care Units are poorly understood. Methods: We classified 1,702 patients admitted to our tertiary and quaternary care center Neuroscience-ICU in 2016 and 2017 into LRM vs. AT groups. We compared demographics, admission diagnosis, goal of care status, readmission rates and managing attending specialty extracted from the medical record between groups. Acute Physiology, Age and Chronic Health Evaluation (APACHE) IVa risk predictive modeling was used to assess comparative risks for ICU and hospital mortality and length of stay between groups. Results: 56.9% of patients admitted to our Neuroscience-ICU in 2016 and 2017 were classified as LRM, whereas 43.1% of patients were classified as AT. While demographically similar, the groups differed significantly in all risk predictive outcome measures [APACHE IVa scores, actual and predicted ICU and hospital mortality (p \u3c 0.0001 for all metrics)]. The most common admitting diagnosis overall, cerebrovascular accident/stroke, was represented in the LRM and AT groups with similar frequency [24.3 vs. 21.3%, respectively (p = 0.15)], illustrating that further differentiating factors like symptom duration, neurologic status and its dynamic changes and neuro-imaging characteristics determine the indication for active treatment vs. observation. Patients with intracranial hemorrhage/hematoma were significantly more likely to receive active treatments as opposed to having a primary focus on monitoring [13.6 vs. 9.8%, respectively (p = 0.017)]. Conclusion: The majority of patients admitted to our Neuroscience ICU (56.9%) had \u3c10% hospital mortality risk and a focus on monitoring, whereas the remaining 43.1% of patients received active treatments in their first ICU day. LRM Patients exhibited significantly lower APACHE IVa scores, ICU and hospital mortality rates compared to AT patients. Observed-over-expected ICU and hospital mortality ratios were better than predicted by APACHE IVa for low risk monitored patients and close to prediction for actively treated patients, suggesting that at least a subset of LRM patients may safely and more cost effectively be cared for in intermediate level care settings

Low Risk Monitoring in Neurocritical Care

Background/Rationale: Patients are admitted to Intensive care units (ICUs) either because they need close monitoring despite a low risk of hospital mortality (LRM group) or to receive ICU specific active treatments (AT group). The characteristics and differential outcomes of LRM patients vs. AT patients in Neurocritical Care Units are poorly understood.Methods: We classified 1,702 patients admitted to our tertiary and quaternary care center Neuroscience-ICU in 2016 and 2017 into LRM vs. AT groups. We compared demographics, admission diagnosis, goal of care status, readmission rates and managing attending specialty extracted from the medical record between groups. Acute Physiology, Age and Chronic Health Evaluation (APACHE) IVa risk predictive modeling was used to assess comparative risks for ICU and hospital mortality and length of stay between groups.Results: 56.9% of patients admitted to our Neuroscience-ICU in 2016 and 2017 were classified as LRM, whereas 43.1% of patients were classified as AT. While demographically similar, the groups differed significantly in all risk predictive outcome measures [APACHE IVa scores, actual and predicted ICU and hospital mortality (p < 0.0001 for all metrics)]. The most common admitting diagnosis overall, cerebrovascular accident/stroke, was represented in the LRM and AT groups with similar frequency [24.3 vs. 21.3%, respectively (p = 0.15)], illustrating that further differentiating factors like symptom duration, neurologic status and its dynamic changes and neuro-imaging characteristics determine the indication for active treatment vs. observation. Patients with intracranial hemorrhage/hematoma were significantly more likely to receive active treatments as opposed to having a primary focus on monitoring [13.6 vs. 9.8%, respectively (p = 0.017)].Conclusion: The majority of patients admitted to our Neuroscience ICU (56.9%) had <10% hospital mortality risk and a focus on monitoring, whereas the remaining 43.1% of patients received active treatments in their first ICU day. LRM Patients exhibited significantly lower APACHE IVa scores, ICU and hospital mortality rates compared to AT patients. Observed-over-expected ICU and hospital mortality ratios were better than predicted by APACHE IVa for low risk monitored patients and close to prediction for actively treated patients, suggesting that at least a subset of LRM patients may safely and more cost effectively be cared for in intermediate level care settings

The ICU Will See You Now: Efficient-equitable Admission Control Policies for a Surgical ICU with Batch Arrivals

Intensive Care Units (ICUs) are frequently the bottleneck in a hospital system, limiting patient flow and negatively impacting profits. This article examines admission control policies for a surgical ICU where patients arrive in batches. This problem is formulated as a Markov Decision Process (MDP) with an objective function that allows for varying degrees of emphasis on efficiency versus equity. Equity concerns are driven by a combination of surgery type and operating surgeon and are captured in a robust manner in the proposed models. A simple and efficient heuristic solution method related to our MDP formulation is proposed that provides a performance guarantee. The proposed admissions policy is applied to a real setting motivated by the cardiothoracic surgical ICU at Mount Sinai Medical Center in New York; the results demonstrate that the ICU can achieve large equity gains with no efficiency losses

Evaluating Tele-ICU Implementation Based on Observed and Predicted ICU Mortality: A Systematic Review and Meta-Analysis

OBJECTIVES: Past studies have examined numerous components of tele-ICU care to decipher which elements increase patient and institutional benefit. These factors include review of the patient chart within 1 hour, frequent collaborative data reviews, mechanisms for rapid laboratory/alert review, and interdisciplinary rounds. Previous meta-analyses have found an overall ICU mortality benefit implementing tele-ICU, however, subgroup analyses found few differences. The purpose of this systematic review and meta-analysis was to explore the effect of tele-ICU implementation with regard to ICU mortality and explore subgroup differences via observed and predicted mortality. DATA SOURCES: We searched PubMed, Cochrane Library, Embase, and European Society of Intensive Care Medicine for articles related to tele-ICU from inception to September 18, 2018. STUDY SELECTION: We included all trials meeting inclusion criteria which looked at the effect of tele-ICU implementation on ICU mortality. DATA EXTRACTION: We abstracted study characteristics, patient characteristics, severity of illness scores, and ICU mortality rates. DATA SYNTHESIS: We included 13 studies from 2,766 abstracts identified from our search strategy. The before-after tele-ICU implementation pooled odds ratio for overall ICU mortality was 0.75 (95% CI, 0.65-0.88; p \u3c 0.001). In subgroup analysis, the pooled odds ratio for ICU mortality between the greater than 1 versus less than 1 observed to predicted mortality ratios was 0.64 (95% CI, 0.52-0.77; p \u3c 0.001) and 0.98 (95% CI, 0.81-1.18; p = 0.81), respectively. Test for interaction was significant (p = 0.002). CONCLUSIONS: After evaluating all included studies, tele-ICU implementation was associated with an overall reduction in ICU mortality. Subgroup analysis suggests that publications exhibiting observed to predicted ICU mortality ratios of greater than 1 before tele-ICU implementation was associated with a reduction in ICU mortality after tele-ICU implementation. No significant ICU mortality reduction was noted in the subgroup of observed to predicted ICU mortality ratio less than 1 before tele-ICU implementation. Future studies should confirm this finding using patient-level data

Telemedicine in the ICU: Clinical Outcomes, Economic Aspects, and Trainee Education

PURPOSE OF REVIEW: The evidence base for telemedicine in the ICU (tele-ICU) is rapidly expanding. The last 2 years have seen important additions to our understanding of when, where, and how telemedicine in the ICU adds value. RECENT FINDINGS: Recent publications and a recent meta-analysis confirm that tele-ICU improves core clinical outcomes for ICU patients. Recent evidence further demonstrates that comprehensive tele-ICU programs have the potential to quickly recuperate their implementation and operational costs and significantly increase case volumes and direct contribution margins particularly if additional logistics and care standardization functions are embedded to optimize ICU bed utilization and reduce complications. Even though the adoption of tele-ICU is increasing and the vast majority of today\u27s medical graduates will regularly use some form of telemedicine and/or tele-ICU, telemedicine modules have not consistently found their way into educational curricula yet. Tele-ICU can be used very effectively to standardize supervision of medical trainees in bedside procedures or point-of-care ultrasound exams, especially during off-hours. Lastly, tele-ICUs routinely generate rich operational data, as well as risk-adjusted acuity and outcome data across the spectrum of critically ill patients, which can be utilized to support important clinical research and quality improvement projects. SUMMARY: The value of tele-ICU to improve patient outcomes, optimize ICU bed utilization, increase financial performance and enhance educational opportunities for the next generation of providers has become more evident and differentiated in the last 2 years

Legal Perspectives on Telemedicine Part 2: Telemedicine in the Intensive Care Unit and Medicolegal Risk

Tele-intensive care unit (tele-ICU) implementation has been shown to improve clinical and financial outcomes. The expansion of this new care delivery model has outpaced the development of its accompanying regulatory framework. In the first part of this commentary we discussed legal and regulatory issues of telemedicine in general and expanded on tele-ICU implementation in particular. Major legal and regulatory barriers to expansion remain, including uncertainty regarding license portability and reimbursement. In this second part we discuss the effects of telemedicine implementation on the various aspects of medicolegal risk and risk mitigation, with a particular focus on tele-ICU. There is a paucity of legal data regarding the effect of tele-ICU implementation on medicolegal risk. We will therefore systematically discuss the effects of tele-ICU on the various root causes of medical error. Given the substantial capital and operational investment that must be undertaken to build and run a tele-ICU, any reduction in risk adds to the financial return on investment and further decreases barriers to implementation