Healthcare Process Modeling to Phenotype Clinician Behaviors for Exploiting the Signal Gain of Clinical Expertise (HPM-ExpertSignals): Development and evaluation of a conceptual framework

Rossetti, Sarah Collins; Knaplund, Chris; Albers, David J.; Dykes, Patricia C.; Kang, Min Jeoung; Korach, Tom Z.; Zhou, Li; Schnock, Kumiko O.; García, José Pérez; Schwartz, James H.; Fu, Li-heng; Klann, Jeffrey G.; Lowenthal, Graham; Cato, Kenrick

doi:10.1093/jamia/ocab006

Cited by 25 publications

(26 citation statements)

References 49 publications

(48 reference statements)

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“…Human ventilator waveform data represent several generating processes, lung physiology, ventilator mechanics, interventions, patient-ventilator interactions, and health care process model effects (Hripcsak and Albers, 2013b;Rossetti et al, 2021). In general, physiological models alone might be missing substantial contributing sources within the data.…”

Section: Introductionmentioning

confidence: 99%

A Damaged-Informed Lung Ventilator Model for Ventilator Waveforms

et al. 2021

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Motivated by a desire to understand pulmonary physiology, scientists have developed physiological lung models of varying complexity. However, pathophysiology and interactions between human lungs and ventilators, e.g., ventilator-induced lung injury (VILI), present challenges for modeling efforts. This is because the real-world pressure and volume signals may be too complex for simple models to capture, and while complex models tend not to be estimable with clinical data, limiting clinical utility. To address this gap, in this manuscript we developed a new damaged-informed lung ventilator (DILV) model. This approach relies on mathematizing ventilator pressure and volume waveforms, including lung physiology, mechanical ventilation, and their interaction. The model begins with nominal waveforms and adds limited, clinically relevant, hypothesis-driven features to the waveform corresponding to pulmonary pathophysiology, patient-ventilator interaction, and ventilator settings. The DILV model parameters uniquely and reliably recapitulate these features while having enough flexibility to reproduce commonly observed variability in clinical (human) and laboratory (mouse) waveform data. We evaluate the proof-in-principle capabilities of our modeling approach by estimating 399 breaths collected for differently damaged lungs for tightly controlled measurements in mice and uncontrolled human intensive care unit data in the absence and presence of ventilator dyssynchrony. The cumulative value of mean squares error for the DILV model is, on average, ≈12 times less than the single compartment lung model for all the waveforms considered. Moreover, changes in the estimated parameters correctly correlate with known measures of lung physiology, including lung compliance as a baseline evaluation. Our long-term goal is to use the DILV model for clinical monitoring and research studies by providing high fidelity estimates of lung state and sources of VILI with an end goal of improving management of VILI and acute respiratory distress syndrome.

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Section: Introductionmentioning

confidence: 99%

A Damaged-Informed Lung Ventilator Model for Ventilator Waveforms

et al. 2021

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“…Our preliminary study using the aforementioned approach has demonstrated that using nursing documentation patterns as an EWS predictor performed similarly to the Modified Early Warning Score (MEWS), one of the most widely used EWSs in clinical settings, which leverages the actual recorded values (eg, a heart rate of 60 BPM) as its predictors [ 12 ]. However, CONCERN was able to detect patient deterioration 42 hours earlier than the MEWS [ 13 ]. CONCERN’s earlier detection capabilities therefore create a more advantageous window of opportunity for clinicians to anticipate and appropriately react to impending patient deterioration.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…The CONCERN CDS system will be triggered on the basis of analytics of nursing documentation through NLP, which indicates the recognition of and concerns about negative patient changes [ 13 , 15 ]. Testing of the predictive model underlying the CONCERN CDS has been conducted on retrospective data and findings have been published, including those that previously highlighted that CONCERN performs similarly to the MEWS with an improved lead time of 42 hours [ 13 , 15 ]. The CONCERN CDS app will alert the interprofessional care team to the patients entering “risky states” to increase team-based situational awareness of these patients and support them as they perform early clinical interventions.…”

Section: Introductionmentioning

confidence: 99%

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The Communicating Narrative Concerns Entered by Registered Nurses (CONCERN) Clinical Decision Support Early Warning System: Protocol for a Cluster Randomized Pragmatic Clinical Trial

Rossetti¹,

Dykes²,

Knaplund³

et al. 2021

JMIR Res Protoc

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Background Every year, hundreds of thousands of inpatients die from cardiac arrest and sepsis, which could be avoided if those patients’ risk for deterioration were detected and timely interventions were initiated. Thus, a system is needed to convert real-time, raw patient data into consumable information that clinicians can utilize to identify patients at risk of deterioration and thus prevent mortality and improve patient health outcomes. The overarching goal of the COmmunicating Narrative Concerns Entered by Registered Nurses (CONCERN) study is to implement and evaluate an early warning score system that provides clinical decision support (CDS) in electronic health record systems. With a combination of machine learning and natural language processing, the CONCERN CDS utilizes nursing documentation patterns as indicators of nurses’ increased surveillance to predict when patients are at the risk of clinical deterioration. Objective The objective of this cluster randomized pragmatic clinical trial is to evaluate the effectiveness and usability of the CONCERN CDS system at 2 different study sites. The specific aim is to decrease hospitalized patients’ negative health outcomes (in-hospital mortality, length of stay, cardiac arrest, unanticipated intensive care unit transfers, and 30-day hospital readmission rates). Methods A multiple time-series intervention consisting of 3 phases will be performed through a 1-year period during the cluster randomized pragmatic clinical trial. Phase 1 evaluates the adoption of our algorithm through pilot and trial testing, phase 2 activates optimized versions of the CONCERN CDS based on experience from phase 1, and phase 3 will be a silent release mode where no CDS is viewable to the end user. The intervention deals with a series of processes from system release to evaluation. The system release includes CONCERN CDS implementation and user training. Then, a mixed methods approach will be used with end users to assess the system and clinician perspectives. Results Data collection and analysis are expected to conclude by August 2022. Based on our previous work on CONCERN, we expect the system to have a positive impact on the mortality rate and length of stay. Conclusions The CONCERN CDS will increase team-based situational awareness and shared understanding of patients predicted to be at risk for clinical deterioration in need of intervention to prevent mortality and associated harm. Trial Registration ClinicalTrials.gov NCT03911687; https://clinicaltrials.gov/ct2/show/NCT03911687 International Registered Report Identifier (IRRID) DERR1-10.2196/30238

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“…Standardizing and reusing such metadata definitions has two major advantages. First, it yields harmonized data sets that allow data exchange between institutions [ 3 , 4 ] and facilitate data analyses, such as multi-site phenotyping [ 5 ] or machine learning [ 6 ]. Second, medical documentation does not have to be developed from scratch reducing costs [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Pragmatic MDR: a metadata repository with bottom-up standardization of medical metadata through reuse

Hegselmann

Storck

Geßner

et al. 2021

BMC Med Inform Decis Mak

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Background The variety of medical documentation often leads to incompatible data elements that impede data integration between institutions. A common approach to standardize and distribute metadata definitions are ISO/IEC 11179 norm-compliant metadata repositories with top-down standardization. To the best of our knowledge, however, it is not yet common practice to reuse the content of publicly accessible metadata repositories for creation of case report forms or routine documentation. We suggest an alternative concept called pragmatic metadata repository, which enables a community-driven bottom-up approach for agreeing on data collection models. A pragmatic metadata repository collects real-world documentation and considers frequent metadata definitions as high quality with potential for reuse. Methods We implemented a pragmatic metadata repository proof of concept application and filled it with medical forms from the Portal of Medical Data Models. We applied this prototype in two use cases to demonstrate its capabilities for reusing metadata: first, integration into a study editor for the suggestion of data elements and, second, metadata synchronization between two institutions. Moreover, we evaluated the emergence of bottom-up standards in the prototype and two medical data managers assessed their quality for 24 medical concepts. Results The resulting prototype contained 466,569 unique metadata definitions. Integration into the study editor led to a reuse of 1836 items and item groups. During the metadata synchronization, semantic codes of 4608 data elements were transferred. Our evaluation revealed that for less complex medical concepts weak bottom-up standards could be established. However, more diverse disease-related concepts showed no convergence of data elements due to an enormous heterogeneity of metadata. The survey showed fair agreement (Kalpha = 0.50, 95% CI 0.43–0.56) for good item quality of bottom-up standards. Conclusions We demonstrated the feasibility of the pragmatic metadata repository concept for medical documentation. Applications of the prototype in two use cases suggest that it facilitates the reuse of data elements. Our evaluation showed that bottom-up standardization based on a large collection of real-world metadata can yield useful results. The proposed concept shall not replace existing top-down approaches, rather it complements them by showing what is commonly used in the community to guide other researchers.

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Healthcare Process Modeling to Phenotype Clinician Behaviors for Exploiting the Signal Gain of Clinical Expertise (HPM-ExpertSignals): Development and evaluation of a conceptual framework

Cited by 25 publications

References 49 publications

A Damaged-Informed Lung Ventilator Model for Ventilator Waveforms

A Damaged-Informed Lung Ventilator Model for Ventilator Waveforms

The Communicating Narrative Concerns Entered by Registered Nurses (CONCERN) Clinical Decision Support Early Warning System: Protocol for a Cluster Randomized Pragmatic Clinical Trial

Pragmatic MDR: a metadata repository with bottom-up standardization of medical metadata through reuse

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