Process mining in healthcare: A literature review Rojas, Eric; Munoz-Gama, Jorge; Sepúlveda, Marcos ...
Journal of biomedical informatics,
June 2016, 2016-06-00, 20160601, Volume:
61
Journal Article
Peer reviewed
Open access
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•Characteristics of case studies where process mining is applied in healthcare.•A reference to the current status of process mining in healthcare.•Trends and challenges regarding the ...future of process mining in healthcare.
Process Mining focuses on extracting knowledge from data generated and stored in corporate information systems in order to analyze executed processes. In the healthcare domain, process mining has been used in different case studies, with promising results. Accordingly, we have conducted a literature review of the usage of process mining in healthcare. The scope of this review covers 74 papers with associated case studies, all of which were analyzed according to eleven main aspects, including: process and data types; frequently posed questions; process mining techniques, perspectives and tools; methodologies; implementation and analysis strategies; geographical analysis; and medical fields. The most commonly used categories and emerging topics have been identified, as well as future trends, such as enhancing Hospital Information Systems to become process-aware. This review can: (i) provide a useful overview of the current work being undertaken in this field; (ii) help researchers to choose process mining algorithms, techniques, tools, methodologies and approaches for their own applications; and (iii) highlight the use of process mining to improve healthcare processes.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Social networking sites (SNSs) have the potential to increase the reach and efficiency of essential public health services, such as surveillance, research, and communication.
The objective of this ...study was to conduct a systematic literature review to identify the use of SNSs for public health research and practice and to identify existing knowledge gaps.
We performed a systematic literature review of articles related to public health and SNSs using PubMed, EMBASE, and CINAHL to search for peer-reviewed publications describing the use of SNSs for public health research and practice. We also conducted manual searches of relevant publications. Each publication was independently reviewed by 2 researchers for inclusion and extracted relevant study data.
A total of 73 articles met our inclusion criteria. Most articles (n=50) were published in the final 2 years covered by our search. In all, 58 articles were in the domain of public health research and 15 were in public health practice. Only 1 study was conducted in a low-income country. Most articles (63/73, 86%) described observational studies involving users or usages of SNSs; only 5 studies involved randomized controlled trials. A large proportion (43/73, 59%) of the identified studies included populations considered hard to reach, such as young individuals, adolescents, and individuals at risk of sexually transmitted diseases or alcohol and substance abuse. Few articles (2/73, 3%) described using the multidirectional communication potential of SNSs to engage study populations.
The number of publications about public health uses for SNSs has been steadily increasing in the past 5 years. With few exceptions, the literature largely consists of observational studies describing users and usages of SNSs regarding topics of public health interest. More studies that fully exploit the communication tools embedded in SNSs and study their potential to produce significant effects in the overall population's health are needed.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Systematic reviews allow health decisions to be informed by the best available research evidence. However, their number is proliferating quickly, and many skills are required to identify all the ...relevant reviews for a specific question.
We screen 10 bibliographic databases on a daily or weekly basis, to identify systematic reviews relevant for health decision-making. Using a machine-based approach developed for this project we select reviews, which are then validated by a network of more than 1000 collaborators. After screening over 1,400,000 records we have identified more than 300,000 systematic reviews, which are now stored in a single place and accessible through an easy-to-use search engine. This makes Epistemonikos the largest database of its kind.
Using a systematic approach, recruiting a broad network of collaborators and implementing automated methods, we developed a one-stop shop for systematic reviews relevant for health decision making.
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As healthcare providers receive fixed amounts of reimbursement for given services under DRG (Diagnosis-Related Groups) payment, DRG codes are valuable for cost monitoring and resource allocation. ...However, coding is typically performed retrospectively post-discharge. We seek to predict DRGs and DRG-based case mix index (CMI) at early inpatient admission using routine clinical text to estimate hospital cost in an acute setting. We examined a deep learning-based natural language processing (NLP) model to automatically predict per-episode DRGs and corresponding cost-reflecting weights on two cohorts (paid under Medicare Severity (MS) DRG or All Patient Refined (APR) DRG), without human coding efforts. It achieved macro-averaged area under the receiver operating characteristic curve (AUC) scores of 0·871 (SD 0·011) on MS-DRG and 0·884 (0·003) on APR-DRG in fivefold cross-validation experiments on the first day of ICU admission. When extended to simulated patient populations to estimate average cost-reflecting weights, the model increased its accuracy over time and obtained absolute CMI error of 2·40 (1·07%) and 12·79% (2·31%), respectively on the first day. As the model could adapt to variations in admission time, cohort size, and requires no extra manual coding efforts, it shows potential to help estimating costs for active patients to support better operational decision-making in hospitals.
Background Diagnosis can often be recorded in electronic medical records (EMRs) as free-text or using a term with a diagnosis code. Researchers, governments, and agencies, including organisations ...that deliver incentivised primary care quality improvement programs, frequently utilise coded data only and often ignore free-text entries. Diagnosis data are reported for population healthcare planning including resource allocation for patient care. This study sought to determine if diagnosis counts based on coded diagnosis data only, led to under-reporting of disease prevalence and if so, to what extent for six common or important chronic diseases. Methods This cross-sectional data quality study used de-identified EMR data from 84 general practices in Victoria, Australia. Data represented 456,125 patients who attended one of the general practices three or more times in two years between January 2021 and December 2022. We reviewed the percentage and proportional difference between patient counts of coded diagnosis entries alone and patient counts of clinically validated free-text entries for asthma, chronic kidney disease, chronic obstructive pulmonary disease, dementia, type 1 diabetes and type 2 diabetes. Results Undercounts were evident in all six diagnoses when using coded diagnoses alone (2.57-36.72% undercount), of these, five were statistically significant. Overall, 26.4% of all patient diagnoses had not been coded. There was high variation between practices in recording of coded diagnoses, but coding for type 2 diabetes was well captured by most practices. Conclusion In Australia clinical decision support and the reporting of aggregated patient diagnosis data to government that relies on coded diagnoses can lead to significant underreporting of diagnoses compared to counts that also incorporate clinically validated free-text diagnoses. Diagnosis underreporting can impact on population health, healthcare planning, resource allocation, and patient care. We propose the use of phenotypes derived from clinically validated text entries to enhance the accuracy of diagnosis and disease reporting. There are existing technologies and collaborations from which to build trusted mechanisms to provide greater reliability of general practice EMR data used for secondary purposes. Keywords: Australia, Data quality, Data reporting, Data standards, Diagnosis, Primary health care
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It is unclear how well self-rated oral health (SROH) reflects actual oral health status in the rural Australian population. Therefore, this study aimed to compare the clinically assessed oral health ...status and SROH of adults living in rural Australia. The data were from 574 participants who took part in the Crossroads II cross-sectional study. Three trained and calibrated dentists evaluated the oral health status of participants based on WHO criteria. SROH was assessed with the question 'Overall, how would you rate the health of your teeth and gums?', with a score ranging from excellent = 5 to poor = 1. A logistic regression analysis (LRA) was performed, allowing us to assess factors associated with SROH. The mean age of participants was 59.2 years (SD 16.3), and 55.3% were female. The key results from the LRA show poorer SROH in those with more missing teeth (OR = 1.05; 95% CI; 1.01-1.08), more decayed teeth (OR = 1.28; 95% CI: 1.11-1.46), and more significant clinical attachment loss of periodontal tissue (6mm or more) (OR = 2.63; 95% CI: 1.29-5.38). This study found an association between negative SROH and clinical indicators used to measure poor oral health status, suggesting that self-rated oral health is an indicator of oral health status. When planning dental healthcare programs, self-reported oral health should be considered a proxy measure for oral health status.
In Chile, more than 500 women die every year from cervical cancer, and a majority of Chilean women are not up-to-date with their Papanicolau (Pap) test. Mobile health has great potential in many ...health areas, particularly in health promotion and prevention. There are no randomized controlled trials in Latin America assessing its use in cervical cancer screening. The 'Development of Mobile Technologies for the Prevention of Cervical Cancer in Santiago, Chile' study aims to determine the efficacy of a text-message intervention on Pap test adherence among Chilean women in the metropolitan region of Santiago.
This study is a parallel randomized-controlled trial of 400 Chilean women aged 25-64 who are non-adherent with current recommendations for Pap test screening. Participants will be randomly assigned to (1) a control arm (usual care) or (2) an intervention arm, where text and voice messages containing information and encouragement to undergo screening will be sent to the women. The primary endpoint is completion of a Pap test within 6 months of baseline assessment, as determined by medical record review at community-based clinics. Medical record reviewers will be blinded to randomization arms. The secondary endpoint is an evaluation of the implementation and usability of the text message intervention as a strategy to improve screening adherence.
This intervention using mobile technology intends to raise cervical cancer screening adherence and compliance among a Chilean population of low and middle-low socioeconomic status. If successful, this strategy may reduce the incidence of cervical cancer.
Clinicaltrials.gov NCT02376023 Registered 2/17/2015. First participant enrolled Feb 22nd 2016.
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Objectives In this overview, we describe theObservational Medical Outcomes Partnership Common Data Model (OMOP-CDM), the established governance processes employed in EMR data repositories, and ...demonstrate how OMOP transformed data provides a lever for more efficient and secure access to electronic medical record (EMR) data by health service providers and researchers.Methods Through pseudonymisation and common data quality assessments, the OMOP-CDM provides a robust framework for converting complex EMR data into a standardised format. This allows for the creation of shared end-to-end analysis packages without the need for direct data exchange, thereby enhancing data security and privacy. By securely sharing de-identified and aggregated data and conducting analyses across multiple OMOP-converted databases, patient-level data is securely firewalled within its respective local site.Results By simplifying data management processes and governance, and through the promotion of interoperability, the OMOP-CDM supports a wide range of clinical, epidemiological, and translational research projects, as well as health service operational reporting.Discussion Adoption of the OMOP-CDM internationally and locally enables conversion of vast amounts of complex, and heterogeneous EMR data into a standardised structured data model, simplifies governance processes, and facilitates rapid repeatable cross-institution analysis through shared end-to-end analysis packages, without the sharing of data.Conclusion The adoption of the OMOP-CDM has the potential to transform health data analytics by providing a common platform for analysing EMR data across diverse healthcare settings.
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•Process mining includes a series of tools that allows the analysis of healthcare activities from the process perspective.•Conformance checking compares observed versus recommended ...processes and could be used to measure adherence to guidelines.•Studies assess adherence to guidelines using few, simple activities; guidelines usually contain tens of recommendations.•Computable guidelines and structured data should facilitate closing the current gap for clinical conformance checking.
Clinical guidelines are recommendations of how to diagnose, treat, and manage a patient’s medical condition. Health organizations must measure adherence to clinical guidelines to enhance the quality of service, but due to the complexity of the medical environment, there is no simple way of measuring adherence to clinical guidelines. This scoping review will systematically assess the criteria used to measure adherence to clinical guidelines in the past 20 years and explore the suitability of using process mining techniques. We will use a workflow protocol based on declarative and temporal constraints to translate the narrative text rules in the publications into a high-level process model. This approach will enable us to explore the main patterns and gaps identified when measuring adherence to clinical guidelines and how they affect the adoption of process mining techniques. The main contributions of this paper are a) a comprehensive analysis of the criteria used for measuring adherence, considering a diverse set of medical conditions b) a framework that will classify the level of complexity of the rules used to measure adherence based on declarative and temporal constraints c) list of key trends and gaps identified in the literature and how they relate to the use of process mining techniques in healthcare.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Introduction
: Clinical guidelines recommend best care pathways for many clinical conditions. When significant data from clinical trials become available, new clinical guidelines are published, ...formalizing the advances in the field. When we consider clinical guidelines as processes of care, we can use process mining techniques to discover how innovations diffuse into healthcare and how they modify the execution of clinical processes.
Methods
: We conducted a study to assess the changes in process execution patterns after a 2013 update of an acute ischemic stroke (AIS) guideline. We used MIMIC-IV as the data source, including patients from 2008 until 2019 hospitalized with an AIS and applied drift detection methods to measure changes in the therapeutic process. We performed statistical tests to determine whether the underlying distribution of events reflects the changes in the guidelines post-2013.
Results
: Ischemic stroke patients show few significant changes in clinical practices, despite an update in guidelines. The positive control group of aortic valve replacement patients shows a significant change in clinical practices surrounding this procedure.
Conclusions
: This study demonstrates the use of drift detection methods as a novel method to study the diffusion of innovations in healthcare settings from a process perspective.
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