Process Modeling and Conformance Checking in Healthcare: A COVID-19 Case Study: Case Study

The discipline of process mining has a solid track record of successful applications to the healthcare domain. Within such research space, we conducted a case study related to the Intensive Care Unit (ICU) ward of the Uniklinik Aachen hospital in Germany. The aim of this work is twofold: developing a normative model representing the clinical guidelines for the treatment of COVID-19 patients, and analyzing the adherence of the observed behavior (recorded in the information system of the hospital) to such guidelines. We show that, through conformance checking techniques, it is possible to analyze the care process for COVID-19 patients, highlighting the main deviations from the clinical guidelines. The results provide physicians with useful indications for improving the process and ensuring service quality and patient satisfaction. We share the resulting model as an open-source BPMN file. © 2023, The Author(s).

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

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.

The Effect of Implementation of Guideline Order Bundles Into a General Admission Order Set on Clinical Practice Guideline Adoption: Quasi-Experimental Study.

BACKGROUND: Clinical practice guidelines (CPGs) and associated order sets can help standardize patient care and lead to higher-value patient care. However, difficult access and poor usability of these order sets can result in lower use rates and reduce the CPGs' impact on clinical outcomes. At our institution, we identified multiple CPGs for general pediatrics admissions where the appropriate order set was used in <50% of eligible encounters, leading to decreased adoption of CPG recommendations. OBJECTIVE: We aimed to determine how integrating disease-specific order groups into a common general admission order set influences adoption of CPG-specific order bundles for patients meeting CPG inclusion criteria admitted to the general pediatrics service. METHODS: We integrated order bundles for asthma, heavy menstrual bleeding, musculoskeletal infection, migraine, and pneumonia into a common general pediatrics order set. We compared pre- and postimplementation order bundle use rates for eligible encounters at both an intervention and nonintervention site for integrated CPGs. We also assessed order bundle adoption for nonintegrated CPGs, including bronchiolitis, acute gastroenteritis, and croup. In a post hoc analysis of encounters without order bundle use, we compared the pre- and postintervention frequency of diagnostic uncertainty at the time of admission. RESULTS: CPG order bundle use rates for incorporated CPGs increased by +9.8% (from 629/856, 73.5% to 405/486, 83.3%) at the intervention site and by +5.1% (896/1351, 66.3% to 509/713, 71.4%) at the nonintervention site. Order bundle adoption for nonintegrated CPGs decreased from 84% (536/638) to 68.5% (148/216), driven primarily by decreases in bronchiolitis order bundle adoption in the setting of the COVID-19 pandemic. Diagnostic uncertainty was more common in admissions without CPG order bundle use after implementation (28/227, 12.3% vs 19/81, 23.4%). CONCLUSIONS: The integration of CPG-specific order bundles into a general admission order set improved overall CPG adoption. However, integrating only some CPGs may reduce adoption of order bundles for excluded CPGs. Diagnostic uncertainty at the time of admission is likely an underrecognized barrier to guideline adherence that is not addressed by an integrated admission order set.

Diagnosis and treatment protocol for COVID-19 patients (Trial Version 9)

To further improve the diagnosis and treatment of COVID-19, China National Health Commission and the National Administration of Traditional Chinese Medicine convened a group of experts to revise the relevant content of the Diagnosis and Treatment Protocol for COVID-19 Patients (Trial Version 8) and developed the Diagnosis and Treatment Protocol for COVID-19 Patients (Trial Version 9), summarizing the etiological characteristics, epidemiological characteristics, pathological changes, clinical features, diagnosis, clinical classification, population with high risk of severe/critical illnesses, early warning predictors for severe/critical illnesses, differential diagnosis, case identification and reporting, treatment, nursing, discharge criteria and precautions after discharge, patient transfer, control of nosocomial infection in medical institutions, and disease prevention.

A Flexible Clinical Decision Support Model and its Application to COVID-19

The positive role of clinical decision support systems based on clinical guidelines in reducing medical errors and improving patient outcomes has been widely recognized. However, the knowledge in clinical guidelines is usually hard-coded into clinical decision support systems, making it difficult for these systems to adapt to the rapid changes of clinical guidelines. Knowledge being hard-coded into the system also means that the system is a black box, and doctors cannot understand the decision-making logic behind the system. These reasons make it difficult for clinical decision support systems to be applied on a large scale. This paper proposes a flexible clinical decision support model, which contains two key parts, namely the knowledge authoring environment and the knowledge execution environment. The transition of knowledge from hard-coded to flexible editing is illustrated in the COVID-19 case. This flexible method will be applied to more complex clinical problems in the future. © 2021 IEEE.

Hospital intranet system is major barrier to paediatric clinical guideline use a cross-sectional single-centre survey of NHS doctors and nurses.

Across clinical specialties, the use of clinical guidelines is integral to maintaining patient safety, reducing variation in clinical practice and optimising patient care. Identifying specific barriers to the effective use of guidelines within individual healthcare settings allows for the implementation of effective strategies to overcome them, and ultimately improvements in patient care. Here, we report a single-centre survey of paediatric doctors and nurses, which formed part of a quality improvement project within the Acute Paediatrics Department of an NHS district general hospital. The primary aim of the study was to explore the perspective and resource barriers paediatrics healthcare staff experience when using local and national clinical guidelines. The secondary aim of the study was to examine the impact of the Covid-19 pandemic on local and national clinical guideline use. We found that local and national guidelines are frequently used by paediatric doctors and nurses, and that they have positive perceptions of guidelines, overall. However, the NHS Trust's Intranet system was identified as a direct barrier to the use of local paediatric clinical guidelines. Staff throughout the UK in the NHS rely on their Intranet system in order to access local guidelines. Our results provide an impetus for interventions within this NHS Trust, and in the many other NHS Trusts with similar Intranet systems, to increase clinical guideline use and, ultimately, improve patient care.

Do the Clinical Management Guidelines for Covid-19 in African Countries Reflect the African Quality Palliative Care Standards? A Review of Current Guidelines.

CONTEXT: Palliative care should be a component of COVID-19 management to relieve suffering, improve patient outcomes and save cost. OBJECTIVES: We aimed to identify and critically appraise the palliative care recommendations within COVID-19 case management guidelines in African countries. METHODS: The study employed systematic guideline review design. All guidelines from any country in Africa, of any language, published between December 2019 and June 2020 were retrieved through online search and email to in-country key contacts. We conducted a content analysis of the palliative care recommendations within the guidelines and appraised the recommendations using African Palliative Care Association standards for providing quality palliative care. RESULTS: We retrieved documents from 29 of 54 African countries. Fifteen documents from 15 countries were included in the final analysis, of which eight countries have identifiable PC recommendations in their COVID-19 management guidelines. Of these eight, only one country (South Sudan) provided comprehensive palliative care recommendations covering the domains of physical, psychological, social and spiritual wellbeing, two (Namibia and Uganda) addressed only physical and psychological wellbeing while the remaining five countries addressed only physical symptom management. CONCLUSIONS: Comprehensive palliative care which addresses physical, psychological, social and spiritual concerns must be prioritized within case management guidelines in African countries.

Prototype Development of an Expert System of Computerized Clinical Guidelines for COVID-19 Diagnosis and Management in Saudi Arabia.

The increasing number of COVID-19 patients has increased health care professionals' workloads, making the management of dynamic patient information in a timely and comprehensive manner difficult and sometimes impossible. Compounding this problem is a lack of health care professionals and trained medical staff to handle the increased number of patients. Although Saudi Arabia has recently improved the quality of its health services, there is still no suitable intelligent system that can help health practitioners follow the clinical guidelines and automated risk assessment and treatment plan remotely, which would allow for the effective follow-up of patients of COVID-19. The proposed system includes five sub-systems: an information management system, a knowledge-based expert system, adaptive learning, a notification and follow-up system, and a mobile tracker system. This study shows that, to control epidemics, there is a method to overcome the shortage of specialists in the management of infections in Saudi Arabia, both today and in the future. The availability of computerized clinical guidance and an up-to-date knowledge base play a role in Saudi health organizations, which may not have to constantly train their physician staff and may no longer have to rely on international experts, since the expert system can offer clinicians all the information necessary to treat their patients.

The Balanced Opioid Initiative: protocol for a clustered, sequential, multiple-assignment randomized trial to construct an adaptive implementation strategy to improve guideline-concordant opioid prescribing in primary care.

BACKGROUND: Rates of opioid prescribing tripled in the USA between 1999 and 2015 and were associated with significant increases in opioid misuse and overdose death. Roughly half of all opioids are prescribed in primary care. Although clinical guidelines describe recommended opioid prescribing practices, implementing these guidelines in a way that balances safety and effectiveness vs. risk remains a challenge. The literature offers little help about which implementation strategies work best in different clinical settings or how strategies could be tailored to optimize their effectiveness in different contexts. Systems consultation consists of (1) educational/engagement meetings with audit and feedback reports, (2) practice facilitation, and (3) prescriber peer consulting. The study is designed to discover the most cost-effective sequence and combination of strategies for improving opioid prescribing practices in diverse primary care clinics. METHODS/DESIGN: The study is a hybrid type 3 clustered, sequential, multiple-assignment randomized trial (SMART) that randomizes clinics from two health systems at two points, months 3 and 9, of a 21-month intervention. Clinics are provided one of four sequences of implementation strategies: a condition consisting of educational/engagement meetings and audit and feedback alone (EM/AF), EM/AF plus practice facilitation (PF), EM/AF + prescriber peer consulting (PPC), and EM/AF + PF + PPC. The study's primary outcome is morphine-milligram equivalent (MME) dose by prescribing clinicians within clinics. The study's primary aim is the comparison of EM/AF + PF + PPC versus EM/AF alone on change in MME from month 3 to month 21. The secondary aim is to derive cost estimates for each of the four sequences and compare them. The exploratory aim is to examine four tailoring variables that can be used to construct an adaptive implementation strategy to meet the needs of different primary care clinics. DISCUSSION: Systems consultation is a practical blend of implementation strategies used in this case to improve opioid prescribing practices in primary care. The blend offers a range of strategies in sequences from minimally to substantially intensive. The results of this study promise to help us understand how to cost effectively improve the implementation of evidence-based practices. TRIAL REGISTRATION: NCT04044521 (ClinicalTrials.gov). Registered 05 August 2019.

2019 Novel coronavirus: where we are and what we know.

There is a current worldwide outbreak of a new type of coronavirus (2019-nCoV), which originated from Wuhan in China and has now spread to 17 other countries. Governments are under increased pressure to stop the outbreak spiraling into a global health emergency. At this stage, preparedness, transparency, and sharing of information are crucial to risk assessments and beginning outbreak control activities. This information should include reports from outbreak sites and from laboratories supporting the investigation. This paper aggregates and consolidates the virology, epidemiology, clinical management strategies from both English and Chinese literature, official news channels, and other official government documents. In addition, by fitting the number of infections with a single-term exponential model, we report that the infection is spreading at an exponential rate, with a doubling period of 1.8 days.