James Fackler

Critical care physician cognitive task analysis: an exploratory study

IntroductionFor better or worse, the imposition of work-hour limitations on house-staff has imperiled continuity and/or improved decision-making. Regardless, the workflow of every physician team in every academic medical centre has been irrevocably altered. We explored the use of cognitive task analysis (CTA) techniques, most commonly used in other high-stress and time-sensitive environments, to analyse key cognitive activities in critical care medicine. The study objective was to assess the usefulness of CTA as an analytical tool in order that physician cognitive tasks may be understood and redistributed within the work-hour limited medical decision-making teams.MethodsAfter approval from each Institutional Review Board, two intensive care units (ICUs) within major university teaching hospitals served as data collection sites for CTA observations and interviews of critical care providers.ResultsFive broad categories of cognitive activities were identified: pattern recognition; uncertainty management; strategic vs. tactical thinking; team coordination and maintenance of common ground; and creation and transfer of meaning through stories.ConclusionsCTA within the framework of Naturalistic Decision Making is a useful tool to understand the critical care process of decision-making and communication. The separation of strategic and tactical thinking has implications for workflow redesign. Given the global push for work-hour limitations, such workflow redesign is occurring. Further work with CTA techniques will provide important insights toward rational, rather than random, workflow changes.

Oxygenation index for extracorporeal membrane oxygenation: is there predictive significance?

Although extracorporeal membrane oxygenation (ECMO) is known to improve survival in neonates with respiratory failure, there has been a significant decrease in the use of ECMO in recent years. Alternative modalities such as high-frequency oscillatory ventilation (HFOV), inhaled nitric oxide (iNO), and surfactant therapy are associated with this decline. The criteria for the initiation of ECMO, developed about 20 years ago, are likely no longer relevant. We examined the predictive significance of the oxygenation index (OI) as a patient entry criterion for ECMO use. We sought a critical OI level predicting death or chronic lung disease (CLD) with and without ECMO use. We also examined whether patients with certain OIs are more likely to have worse outcomes. One hundred and seventy-four term-newborn admissions between 1995 and 2000 requiring mechanical ventilation were enrolled in the study. Receiver operating curve analysis was performed to find a cutoff value of OI for ECMO initiation. Mortality rates and CLD probability were compared to the worst OIs. Our 6-year ECMO administration experience showed that an OI of 33.2 is a suitable cutoff value for ECMO initiation with high sensitivity and specificity as a predictive criterion. The critical OI value associated with the CLD risk when ECMO is not used is in the 40s. OI is a good predictor of CLD; the probability of CLD increases with higher OIs. Our data support the trend toward the use of new interventions over ECMO, especially for patients with OI scores of less than 33.2. Only when the probability of ventilator-associated lung injury becomes significant is it better to consider ECMO than conventional modalities.

Pediatric disposition classification (Reverse Triage) system to create surge capacity

BACKGROUNDnCritically insufficient pediatric hospital capacity may develop during a disaster or surge event. Research is lacking on the creation of pediatric surge capacity. A system of reverse triage, with early discharge of hospitalized patients, has been developed for adults and shows great potential but is unexplored in pediatrics.nnnMETHODSnWe conducted an evidence-based modified-Delphi consensus process with 25 expert panelists to derive a disposition classification system for pediatric inpatients on the basis of risk tolerance for a consequential medical event (CME). For potential validation, critical interventions (CIs) were derived and ranked by using a Likert scale to indicate CME risk should the CI be withdrawn or withheld for early disposition.nnnRESULTSnPanelists unanimously agreed on a 5-category risk-based disposition classification system. The panelists established upper limit (mean) CME risk for each category as <2% (interquartile range [IQR]: 1-2%); 7% (5-10%), 18% (10-20%), 46% (20-65%), and 72% (50-90%), respectively. Panelists identified 25 CIs with varying degrees of CME likelihood if withdrawn or withheld. Of these, 40% were ranked high risk (Likert scale mean ≥7) and 32% were ranked modest risk (≤3).nnnCONCLUSIONSnThe classification system has potential for an ethically acceptable risk-based taxonomy for pediatric inpatient reverse triage, including identification of those deemed safe for early discharge during surge events.

Use of risk stratification indices to predict mortality in critically ill children

The complexity and high cost of neonatal and pediatric intensive care has generated increasing interest in developing measures to quantify the severity of patient illness. While these indices may help improve health care quality and benchmark mortality across hospitals, comprehensive understanding of the purpose and the factors that influenced the performance of risk stratification indices is important so that they can be compared fairly and used most appropriately. In this review, we examined 19 indices of risk stratification used to predict mortality in critically ill children and critically analyzed their design, limitations, and purposes. Some pediatric and neonatal models appear well-suited for institutional benchmarking purposes, with relatively brief data acquisition times, limited potential for treatment-related bias, and reliance on diagnostic variables that permit adjustment for case mix. Other models are more suitable for use in clinical trials, as they rely on physiologic variables collected over an extended period, to better capture the interaction between organ systems function and specific therapeutic interventions in acutely ill patients. Irrespective of their clinical or research applications, risk stratification indices must be periodically recalibrated to adjust for changes in clinical practice in order to remain valid outcome predictors in pediatric intensive care units. Longitudinal auditing, education, training, and guidelines development are also critical to ensure fidelity and reproducibility in data reporting. Conclusion: Risk stratification indices are valid tools to describe intensive care unit population and explain differences in mortality.

Effect of Reverse Triage on Creation of Surge Capacity in a Pediatric Hospital

Importance The capacity of pediatric hospitals to provide treatment to large numbers of patients during a large-scale disaster remains a concern. Hospitals are expected to function independently for as long as 96 hours. Reverse triage (early discharge), a strategy that creates surge bed capacity while conserving resources, has been modeled for adults but not pediatric patients. Objective To estimate the potential of reverse triage for surge capacity in an academic pediatric hospital. Design, Setting, and Participants In this retrospective cohort study, a blocked, randomized sampling scheme was used including inpatients from 7 units during 196 mock disaster days distributed across the 1-year period from December 21, 2012, through December 20, 2013. Patients not requiring any critical interventions for 4 successive days were considered to be suitable for low-risk immediate reverse triage. Data were analyzed from November 1, 2014, through November 21, 2016. Main Outcomes and Measures Proportionate contribution of reverse triage to the creation of surge capacity measured as a percentage of beds newly available in each unit and in aggregate. Results Of 3996 inpatients, 501 were sampled (268 boys [53.5%] and 233 girls [46.5%]; mean [SD] age, 7.8 [6.6] years), with 10.8% eligible for immediate low-risk reverse triage and 13.2% for discharge by 96 hours. The psychiatry unit had the most patients eligible for immediate reverse triage (72.7%; 95% CI, 59.6%-85.9%), accounting for more than half of the reverse triage effect. The oncology (1.3%; 95% CI, 0.0%-3.9%) and pediatric intensive care (0%) units had the least effect. Gross surge capacity using all strategies (routine patient discharges, full use of staffed and unstaffed licensed beds, and cancellation of elective and transfer admissions) was estimated at 57.7% (95% CI, 38.2%-80.2%) within 24 hours and 84.1% (95% CI, 63.9%-100%) by day 4. Net surge capacity, estimated by adjusting for routine emergency department admissions, was about 50% (range, 49.1%-52.6%) throughout the 96-hour period. By accepting higher-risk patients only (considering only major critical interventions as limiting), reverse triage would increase surge capacity by nearly 50%. Conclusions and Relevance Our estimates indicate considerable potential pediatric surge capacity by using combined strategic initiatives. Reverse triage adds a meaningful but modest contribution and may depend on psychiatric space. Large volumes of pediatric patients discharged early to the community during disasters could challenge pediatricians owing to the close follow-up likely to be required.

Why doesn't healthcare embrace simulation and modeling?: what would it take?

Physicians do modeling - every day, all day. Its just that its done with hideous imprecision making cross-patient conclusions hazardous and extensibility impossible. Most of these mental models are devoid of formal logic. Rather, these mental models are patterns matched in a specific patient with a specific problem(s) based on a clinicians experience and “book-knowledge”. We will explore some of the steps that could contribute to the broader acceptance of mathematical models in health care. We will distinguish models that impact the care of the individual patient from that of a larger population.

Macrocognition in the Healthcare Built Environment (mHCBE): A Focused Ethnographic Study of “Neighborhoods” in a Pediatric Intensive Care Unit

Objectives: The objectives of this research were to describe the interactions (formal and informal), in which macrocognitive functions occur and their location on a pediatric intensive care unit, to describe challenges and facilitators of macrocognition using space syntax constructs (openness, connectivity, and visibility), and to analyze the healthcare built environment (HCBE) using those constructs to explicate influences on macrocognition. Background: In high reliability, complex industries, macrocognition is an approach to develop new knowledge among interprofessional team members. Although macrocognitive functions have been analyzed in multiple healthcare settings, the effect of the HCBE on those functions has not been directly studied. The theoretical framework, “macrocognition in the healthcare built environment” (mHCBE) addresses this relationship. Method: A focused ethnographic study was conducted including observation and focus groups. Architectural drawing files used to create distance matrices and isovist field view analyses were compared to panoramic photographs and ethnographic data. Results: Neighborhoods comprised of corner configurations with maximized visibility enhanced team interactions as well as observation of patients, offering the greatest opportunity for informal situated macrocognitive interactions (SMIs). Conclusions: Results from this study support the intricate link between macrocognitive interactions and space syntax constructs within the HCBE. These findings help increase understanding of how use of the framework of Macrocognition in the HCBE can improve design and support adaptation of interprofessional team practices, maximizing macrocognitive interaction opportunities for patient, family, and team safety and quality.

Mapping the Flow of Pediatric Trauma Patients Using Process Mining

BACKGROUNDnInhospital pediatric trauma care typically spans multiple locations, which influences the use of resources, that could be improved by gaining a better understanding of the inhospital flow of patients and identifying opportunities for improvement.nnnOBJECTIVESnTo describe a process mining approach for mapping the inhospital flow of pediatric trauma patients, to identify and characterize the major patient pathways and care transitions, and to identify opportunities for patient flow and triage improvement.nnnMETHODSnFrom the trauma registry of a level I pediatric trauma center, data were extracted regarding the two highest trauma activation levels, Alpha (nu2009=u2009228) and Bravo (nu2009=u20091,713). An event log was generated from the admission, discharge, and transfer data from which patient pathways and care transitions were identified and described. The Flexible Heuristics Miner algorithm was used to generate a process map for the cohort, and separate process maps for Alpha and Bravo encounters, which were assessed for conformance when fitness value was less than 0.950, with the identification and comparison of conforming and nonconforming encounters.nnnRESULTSnThe process map for the cohort was similar to a validated process map derived through qualitative methods. The process map for Bravo encounters had a relatively low fitness of 0.887, and 96 (5.6%) encounters were identified as nonconforming with characteristics comparable to Alpha encounters. In total, 28 patient pathways and 20 care transitions were identified. The top five patient pathways were traversed by 92.1% of patients, whereas the top five care transitions accounted for 87.5% of all care transitions. A larger-than-expected number of discharges from the pediatric intensive care unit (PICU) were identified, with 84.2% involving discharge to home without the need for home care services.nnnCONCLUSIONnProcess mining was successfully applied to derive process maps from trauma registry data and to identify opportunities for trauma triage improvement and optimization of PICU use.

Beyond Current HIMS: Future Visions and a Roadmap

This chapter explores the new technologies being developed to support clinical decision-making and care delivery and presents a roadmap for the safer and more effective next generation health information management systems. Innovative applications (apps) are being built today using twenty-first century web technologies and cloud-based architectural platforms. A basic premise for these new care delivery support systems is that they are built as vendor-agnostic, patient-centric apps and use the current electronic medical records (EHR) and/or clinical systems as infrastructure. The roadmap presented here projects that these new clinical decision support (CDS) and care delivery apps will be built upon but outside of the current electronic health record (EHR) systems – referred to here under the general term of health information management systems (HIMS). Current HIMS will play a valuable role in the data collection process. However, it must acknowledged that the data collected by today’s HIMS systems must be augmented with data not now routinely captured. The challenge is not to undo the mainstream HIMSs but rather to use the data they record, augment the data as is possible and build apps (including novel visualizations) on the vendor-agnostic patient-centric data. A self-perpetuating cycle must be created so that as more apps are built, users (clinicians and consumers) will clamor for more. As more apps are used they will become more refined. As this cycle spins, there can be that optimism the ultimate goal – improved patient care – will be realized.