The future development of intensive care quality indicators – a methods paper

Kumpf, Oliver; Nothacker, Monika; Braun, J.; Muhl, Elke

doi:10.3205/000285

Cited by 4 publications

(5 citation statements)

References 32 publications

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“…An automated integration of guideline recommendations with clinical data as done by our developed systems holds several advantages: The system can provide a certain kind of decision support during individual patient treatment by pointing to applicable guideline recommendations, which the treating health care professionals might either not be aware of or which are known but whose applicability might go unnoticed. Additionally, the monitoring of guideline recommendation adherence across groups of patients provides possibilities for their use as quality and performance indicators [42,43] that can easily be monitored in real-time using a system as we propose it here. Another similar aspect could be the application of the system to monitor process implementation of new guidelines and to provide clinical insights in applicability of guideline recommendations that are useful for guideline updates.…”

Section: Discussionmentioning

confidence: 99%

Design and implementation of a system for automated monitoring of adherence to evidenced-based clinical guideline recommendations

Lichtner

Spies

Jurth

et al. 2022

Preprint

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Background Clinical practice guidelines are systematically developed statements intended to optimize patient care. However, a gap-less implementation of guideline recommendations requires health care personnel not only to be aware of the recommendations and to support their content, but also to recognize every situation in which they are applicable. To not miss situations in which guideline recommendations should be applied, computerized clinical decision support could be given through a system that allows an automated monitoring of adherence to clinical guideline recommendation in individual patients. Objectives (1) To derive the requirements for a system that allows to monitor the adherence to evidence-based clinical guideline recommendations in individual patients, and based on these requirements, (2) to implement a software prototype that integrates clinical guideline recommendations with individual patient data and (3) to demonstrate the prototype's utility on a COVID-19 intensive care treatment recommendation. Methods We performed a work process analysis with experienced intensive care clinicians to develop a conceptual model of how to support guideline adherence monitoring in clinical routine and identified which steps in the model could be supported electronically. We then identified the core requirements of a software system for supporting recommendation adherence monitoring in a consensus-based requirements analysis within loosely structured focus group work of key stakeholders (clinicians, guideline developers, health data engineers, software developers). Based on these requirements, we implemented a prototype and demonstrated its functionality by integrating clinical data with a treatment recommendation. Results Based on our conceptual flow chart model of recommendation adherence monitoring in clinical routine, we identified four main requirements of a software system for automated support of recommendation adherence monitoring of in-hospital patients: (i) Ability to interpret guideline recommendations' semantics and logics, (ii) integration of clinical routine data from various underlying data structures, (iii) automatic adoption of new or updated guideline recommendations, and (iv) user interfaces optimized for distinct groups of users. Using a prototype implementation that fulfills these requirements, we demonstrate how such a system could be applied to monitor guideline recommendation adherence over time in clinical patients. Conclusions The four main requirements identified through our model-based analysis represent the most important aspects that need to be considered when developing a clinical decision support system for monitoring the adherence to evidence-based clinical guideline recommendations in individual patients. As each of the requirements corresponds to a different expertise (guideline development, health data engineering, software development, patient treatment), a modularized software architecture separated by area of required expertise seems favorable. Our prototype successfully demonstrates how such a modular architecture can be implemented to allow real-time monitoring of guideline recommendation adherence. This prototype, which we released as open source to invigorate collaboration, could serve as a basis for further development to integrate guideline recommendations with clinical information systems.

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Section: Discussionmentioning

confidence: 99%

Design and implementation of a system for automated monitoring of adherence to evidenced-based clinical guideline recommendations

Lichtner

Spies

Jurth

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…An automated integration of guideline recommendations with clinical data, as done by our developed systems, has several advantages: the system can provide a certain kind of decision support during individual patient treatment by pointing to applicable guideline recommendations, which the treating health care professionals might either not be aware of or which are known but whose applicability might go unnoticed. In addition, the monitoring of guideline recommendation adherence across groups of patients provides possibilities for their use as quality and performance indicators [51,52] that can easily be monitored in real time using a system, as we propose here. Another similar aspect could be the application of the system to monitor the process implementation of new guidelines and to provide clinical insights into the applicability of guideline recommendations that are useful for guideline updates.…”

Section: Principal Findingsmentioning

confidence: 99%

Automated Monitoring of Adherence to Evidenced-Based Clinical Guideline Recommendations: Design and Implementation Study

Lichtner¹,

Spies²,

Jurth³

et al. 2023

J Med Internet Res

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Background Clinical practice guidelines are systematically developed statements intended to optimize patient care. However, a gapless implementation of guideline recommendations requires health care personnel not only to be aware of the recommendations and to support their content but also to recognize every situation in which they are applicable. To not miss situations in which recommendations should be applied, computerized clinical decision support can be provided through a system that allows an automated monitoring of adherence to clinical guideline recommendations in individual patients. Objective This study aims to collect and analyze the requirements for a system that allows the monitoring of adherence to evidence-based clinical guideline recommendations in individual patients and, based on these requirements, to design and implement a software prototype that integrates guideline recommendations with individual patient data, and to demonstrate the prototype’s utility in treatment recommendations. Methods We performed a work process analysis with experienced intensive care clinicians to develop a conceptual model of how to support guideline adherence monitoring in clinical routine and identified which steps in the model could be supported electronically. We then identified the core requirements of a software system to support recommendation adherence monitoring in a consensus-based requirements analysis within the loosely structured focus group work of key stakeholders (clinicians, guideline developers, health data engineers, and software developers). On the basis of these requirements, we designed and implemented a modular system architecture. To demonstrate its utility, we applied the prototype to monitor adherence to a COVID-19 treatment recommendation using clinical data from a large European university hospital. Results We designed a system that integrates guideline recommendations with real-time clinical data to evaluate individual guideline recommendation adherence and developed a functional prototype. The needs analysis with clinical staff resulted in a flowchart describing the work process of how adherence to recommendations should be monitored. Four core requirements were identified: the ability to decide whether a recommendation is applicable and implemented for a specific patient, the ability to integrate clinical data from different data formats and data structures, the ability to display raw patient data, and the use of a Fast Healthcare Interoperability Resources–based format for the representation of clinical practice guidelines to provide an interoperable, standards-based guideline recommendation exchange format. Conclusions Our system has advantages in terms of individual patient treatment and quality management in hospitals. However, further studies are needed to measure its impact on patient outcomes and evaluate its resource effectiveness in different clinical settings. We specified a modular software architecture that allows experts from different fields to work independently and focus on their area of expertise. We have released the source code of our system under an open-source license and invite for collaborative further development of the system.

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“…Qualitätsindikatoren (QI) sind ein wegweisendes Messinstrument, um Qualitätsausprägungen darstellen zu können und vergleichbar zu machen. Für die Evaluation von medizinischen Prozessen und ihrer Qualität ist die Erhebung von Kennzahlen ein wichtiges Element [6,18]. Die Definition dieser Qualitätskennzahlen bzw.…”

Section: Intensivmedizinische Qualitätsindikatoren Der Diviunclassified

“…B. Erhalt eines Mindeststandards vs. Qualitätsverbesserung). Weiterhin ist ihre Erhebung nicht in jeder Situation einfach, da sie möglichst ohne zusätzlichen Aufwand und mit hoher Datenqualität erfolgen soll [18]. Zuletzt ist die Bewertungauch in Bezug auf die erwähnten Zieleim Behandlungskontext oft nicht einfach zu interpretieren.…”

Section: Intensivmedizinische Qualitätsindikatoren Der Diviunclassified