Tracing frequent users of regional care services using emergency medical services data:a networked approach

Objectives: This study shows how a networked approach relying on ‘real-world’ emergency medical services (EMS) records might contribute to tracing frequent users of care services on a regional scale. Their tracing is considered of importance for policy-makers and clinicians, since they represent a considerable workload and use of scarce resources. While existing approaches for data collection on frequent users tend to limit scope to individual or associated care providers, the proposed approach exploits the role of EMS as the network’s ‘ferryman’ overseeing and recording patient calls made to an entire network of care providers. Design: A retrospective study was performed analysing 2012–2017 EMS calls in the province of Drenthe, the Netherlands. Using EMS data, benefits of the networked approach versus existing approaches are assessed by quantifying the number of frequent users and their associated calls for various categories of care providers. Main categories considered are hospitals, nursing homes and EMS. Setting: EMS in the province of Drenthe, the Netherlands, serving a population of 491 867. Participants: Analyses are based on secondary patient data from EMS records, entailing 212 967 transports and 126 758 patients, over 6 years (2012–2017). Results: Use of the networked approach for analysing calls made to hospitals in Drenthe resulted in a 20% average increase of frequent users traced. Extending the analysis by including hospitals outside Drenthe increased ascertainment by 28%. Extending to all categories of care providers, inside Drenthe, and subsequently, irrespective of their location, resulted in an average increase of 132% and 152% of frequent users identified, respectively. Conclusions: Many frequent users of care services are network users relying on multiple regional care providers, possibly representing inefficient use of scarce resources. Network users are effectively and efficiently traced by using EMS records offering high coverage of calls made to regional care provider

Integración de técnicas de minería de procesos para la detección de variabilidad en procesos hospitalarios desde sistemas automatizados

Los sistemas de información hospitalaria cuentan con un volumen importante de datos, sin embargo, carecen de mecanismos que permitan analizar la ejecución de los procesos e identificar variabilidad. La variabilidad puede observarse en prácticamente cada paso del proceso asistencial y a varios niveles de agrupación: poblacional e individual. Desde el punto de vista poblacional se comparan tasas de realización de un procedimiento clínico, como pueden ser intervenciones quirúrgicas o ingresos hospitalarios en un período de tiempo. Las técnicas de minería de procesos analizan los datos reales de sistemas informáticos y son útiles para la detección de variabilidad en la ejecución de los procesos de negocio. La presente investigación propone la aplicación de técnicas de minería de procesos, seleccionadas a partir de un riguroso estudio del estado del arte, para el análisis de los procesos hospitalarios desde sus sistemas de información y materializadas en un modelo computacional. El Modelo para la Detección de Variabilidad (MDV) se instrumentó exitosamente en el sistema XAVIA HIS desarrollado por la Universidad de las Ciencias Informáticas UCI, donde fueron adaptadas e integradas las técnicas de minería de procesos. El modelo MDV contribuye al proceso de informatización de la salud en Cuba. La solución propicia la utilización de una tecnología emergente en áreas como la industrial y empresarial en el entorno sanitario. Esta beneficia importantes funciones gerenciales como la gestión, control y planificación de recursos y servicios sanitarios.Palabras clave: Sistema de información hospitalaria; minería de procesos; modelo de proceso; variabilidad.</p

Can process mining automatically describe care pathways of patients with long-term conditions in UK primary care? A study protocol

Introduction In the UK, primary care is seen as the optimal context for delivering care to an ageing population with a growing number of long-term conditions. However, if it is to meet these demands effectively and efficiently, a more precise understanding of existing care processes is required to ensure their configuration is based on robust evidence. This need to understand and optimise organisational performance is not unique to healthcare, and in industries such as telecommunications or finance, a methodology known as ‘process mining’ has become an established and successful method to identify how an organisation can best deploy resources to meet the needs of its clients and customers. Here and for the first time in the UK, we will apply it to primary care settings to gain a greater understanding of how patients with two of the most common chronic conditions are managed. Methods and analysis The study will be conducted in three phases; first, we will apply process mining algorithms to the data held on the clinical management system of four practices of varying characteristics in the West Midlands to determine how each interacts with patients with hypertension or type 2 diabetes. Second, we will use traditional process mapping exercises at each practice to manually produce maps of care processes for the selected condition. Third, with the aid of staff and patients at each practice, we will compare and contrast the process models produced by process mining with the process maps produced via manual techniques, review differences and similarities between them and the relative importance of each. The first pilot study will be on hypertension and the second for patients diagnosed with type 2 diabetes

Augmenting K-Means Clustering With Qualitative Data to Discover the Engagement Patterns of Older Adults With Multimorbidity When Using Digital Health Technologies: Proof-of-Concept Trial

Background:Multiple chronic conditions (multimorbidity) are becomingmore prevalent among aging populations. Digital health technologies have thepotential to assist in the self-management of multimorbidity, improving theawareness and monitoring of health and well-being, supporting a betterunderstanding of the disease, and encouraging behavior change.Objective:The aim of this study was to analyze how 60 older adults(mean age 74, SD 6.4; range 65-92 years) with multimorbidity engaged withdigital symptom and well-being monitoring when using a digital health platformover a period of approximately 12 months. Methods:Principal component analysis and clustering analysis wereused to group participants based on their levels of engagement, and the dataanalysis focused on characteristics (eg, age, sex, and chronic healthconditions), engagement outcomes, and symptom outcomes of the differentclusters that were discovered.Results:Three clusters were identified: the typical user group, theleast engaged user group, and the highly engaged user group. Our findings showthat age, sex, and the types of chronic health conditions do not influenceengagement. The 3 primary factors influencing engagement were whether the samedevice was used to submit different health and well-being parameters, thenumber of manual operations required to take a reading, and the daily routineof the participants. The findings also indicate that higher levels ofengagement may improve the participants’ outcomes (eg, reduce symptomexacerbation and increase physical activity).Conclusions:The findings indicate potential factors that influence olderadult engagement with digital health technologies for home-based multimorbidityself-management. The least engaged user groups showed decreased health andwell-being outcomes related to multimorbidity self-management. Addressing thefactors highlighted in this study in the design and implementation ofhome-based digital health technologies may improve symptom management andphysical activity outcomes for older adults self-managing multimorbidity.</p

Predicting Illness and Type of Treatment from Digital Health Records

Kasvavad kulud tervishoius ning samaaegne töötava populatsiooni kahanemine on kriitliine probleem kõikjal arenenud maailmas. Ühest küljest on paratamatu, et uued ravimid ja meetodid on kallid, teisest küljest on võimalik vähendada välditavaid kulutusi parema plaanimise ja ennetustööga. Enamik haiglad salvestavad digitaalselt kõik, mis patsiendiga ravi jooksul toimub ja Eestis esitatakse kõik raviarved ka Eesti Haigekassale (HK) hüvitamiseks. Käesolevas töös kasutatakse HK andmeid ehitamaks mudelit, mille abil on võimalik tuletada erinevad raviprotsessid, mida patsientide ravimisel kasutatakse ning samuti ka ennustada patsientide hulka, kes tulevikus vastavat ravi vajavad. Selline mudel võiks olla kasulik suunamaks otsuseid vahendite jaotamisel ja ennetustöö suunamisel.The rising costs of healthcare and decreasing size of the working population is a dire problem in most of the developed world. While it is inevitable that new methods are costly, it is possible to reduce avoidable expenses by better planning and prevention. Most hospitals keep digital records of everything that happens to a patient during their treatment and in Estonia all medical bills are also presented to the National Health Insurance Fund (NHIF) for reimbursement. In this work the data from NHIF is used to build a model that as the first step uncovers the different clinical pathways followed for the treatment of patients with an illness. As a second step the model is used to predict the number of patients that will be provided the uncovered treatments in the future. The output of such a model could be a valuable asset for planning resource allocation and preventative health care

The neglected contexts and outcomes of evidence-based management:A systematic scoping review in hospital settings

PURPOSE: The coronavirus disease 2019 (COVID-19) pandemic highlighted the necessity of practicing Evidence-based Management (EBMgt) as an approach to decision-making in hospital settings. The literature, however, provides limited insight into the process of EBMgt and its contextual nuances. Such insight is critical for better leveraging EBMgt in practice. Therefore, the authors' aim was to integrate the literature on the process of EBMgt in hospital settings, identify the gaps in knowledge and delineate areas for future research. DESIGN/METHODOLOGY/APPROACH: The authors conducted a systematic scoping review using an innovative methodology that involved two systematic searches. First using EBMgt terminology and second using terminology associated with the EBMgt concept, which the authors derived from the first search. FINDINGS: The authors identified 218 relevant articles, which using content analysis, they mapped onto the grounded model of the EBMgt process; a novel model of the EBMgt process developed by Sahakian and colleagues. The authors found that the English language literature provides limited insight into the role of managers' perceptions and motives in EBMgt, the practice of EBMgt in Global South countries, and the outcomes of EBMgt. Overall, this study’s findings indicated that aspects of the decision-maker, context and outcomes have been neglected in EBMgt. ORIGINALITY/VALUE: The authors contributed to the EBMgt literature by identifying these gaps and proposing future research areas and to the systematic review literature by developing a novel scoping review method

From Personalized Medicine to Population Health: A Survey of mHealth Sensing Techniques

Mobile Sensing Apps have been widely used as a practical approach to collect behavioral and health-related information from individuals and provide timely intervention to promote health and well-beings, such as mental health and chronic cares. As the objectives of mobile sensing could be either \emph{(a) personalized medicine for individuals} or \emph{(b) public health for populations}, in this work we review the design of these mobile sensing apps, and propose to categorize the design of these apps/systems in two paradigms -- \emph{(i) Personal Sensing} and \emph{(ii) Crowd Sensing} paradigms. While both sensing paradigms might incorporate with common ubiquitous sensing technologies, such as wearable sensors, mobility monitoring, mobile data offloading, and/or cloud-based data analytics to collect and process sensing data from individuals, we present a novel taxonomy system with two major components that can specify and classify apps/systems from aspects of the life-cycle of mHealth Sensing: \emph{(1) Sensing Task Creation \& Participation}, \emph{(2) Health Surveillance \& Data Collection}, and \emph{(3) Data Analysis \& Knowledge Discovery}. With respect to different goals of the two paradigms, this work systematically reviews this field, and summarizes the design of typical apps/systems in the view of the configurations and interactions between these two components. In addition to summarization, the proposed taxonomy system also helps figure out the potential directions of mobile sensing for health from both personalized medicines and population health perspectives.Comment: Submitted to a journal for revie

Sistematização da análise de conformidade dos processos na área de saúde : Sariah Ester Torno Mourão

Orientadores : Prof. Dr. Ricardo Mendes Junior, Prof. Dr. José Eduardo Pécora Junior, Profª Drª Adriana de Paula Lacerda SantosCoorientador : Prof. Dr. Eduardo Alves Portela SantosDissertação (mestrado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Engenharia de Produção. Defesa: Curitiba, 24/02/2017Inclui referências : f. 155-164Resumo: Os processos de tratamento médico são universalmente realizados de acordo com as orientações clínicas. No entanto, existe uma lacuna entre estas orientações e a prática clínica real, ou seja, há diferenças entre as atividades executadas e as atividades recomendadas nos procedimentos. Portanto, um desafio para o setor de gestão da saúde é abordar essa lacuna. Existindo assim uma necessidade de métodos que possam medir a adesão do comportamento real do processo no que diz respeito ao comportamento esperado; identificar onde os desvios acontecem com mais frequência e; produzir resultados que possam ser facilmente compreendidos por médicos para destacar as causas mais comuns dos desvios identificados. Este é um dos objetivos da mineração de processos. A mineração de processos fornece uma imagem real do que está acontecendo, explicitando diversas perspectivas acerca das atividades, recursos e informações dos processos. Esta área de estudo está preocupada com a descoberta, monitoramento e melhoria dos processos operacionais por meio da extração de conhecimento a partir de registros gerados pelos sistemas de informação. O principal objetivo desta pesquisa é sistematizar a análise de conformidade dos processos na área da saúde tendo como estudo empírico quantitativo os processos de tratamento de pacientes com Acidente Vascular Cerebral Isquêmico (AVCI), elegíveis para trombólise endovenosa, intraarterial e mecânica, do Hospital Municipal São José, localizado na cidade de Joinville - Santa Catarina. De acordo com os estudos da Global Burden of Disease 2013 - Mortality and Causes of Death (MURRAY et al., 2015), o Acidente Vascular Cerebral (AVC) é uma das mais importantes doenças crônicas, em termos de abrangência, sendo a terceira principal causa de morte no Brasil e a principal causa de incapacidade no mundo. A sistematização proposta busca, auxiliar pesquisadores e instituições de saúde na aplicação das técnicas de descoberta e análise de conformidade dos processos na área da saúde, de modo que tais técnicas possam contribuir para a melhoria do fluxo de atividades em estabelecimentos de saúde e, consequentemente, gerar um efeito positivo sobre a saúde no Brasil. É formada por nove etapas que envolvem: o conhecimento do Sistema de Informação Hospitalar (SIH) e da base de dados; a preparação da base de dados para aplicação das técnicas de mineração de processos; o estudo dos protocolos assistenciais, procedimentos operacionais padrão, instruções de trabalho e outros documentos feitos pela instituição de saúde para o processo selecionado; o estudo das diretrizes clínicas, regulamentos, normas, leis e outros documentos que envolvem o processo escolhido; a transcrição dos documentos selecionados em notação Business Process Management and Notation; a correlação entre os protocolos assistenciais, procedimentos operacionais padrão, instruções de trabalho e outros documentos com as diretrizes clínicas, regulamentos, normas, leis e outros documentos; e por fim as análises quantitativas usando técnicas de mineração de processos, tais como, descoberta do modelo do processo real e análises de conformidade para confrontar os modelos dos processos com os registros de eventos. Palavras-chave: Mineração de Processos. Descoberta. Análise de Conformidade. Sistematização. Área da Saúde. Acidente Vascular Cerebral.Abstract: The medical treatment processes are universally performed according to clinical guidelines. However, there is a gap between these guidelines and the actual clinical practice, that is, there are differences between the activities performed and the activities recommended in the procedures. Therefore, a challenge for the health management sector is to address the gap between actual clinical processes and the recommendations given in the procedures. Thus, there is an urgent need for methods that can: measure adherence to the actual behavior of the process with respect to expected behavior; identify where deviations occur most often and; produce results that can be easily understood by physicians to highlight the most common causes of identified deviations. This is one of the objectives of process mining. Process mining provides a true picture of what is happening, spelling out diverse perspectives on process activities, resources, and information. This area of study is concerned with the discovery, monitoring and improvement of operational processes through the extraction of knowledge from records generated by information systems. The main objective of this research is to systematize the conformity analysis of the health processes, having as a quantitative empirical study the procedures for the treatment of patients with Ischemic Stroke, eligible for intravenous, intraarterial and mechanical thrombolysis, of the São José Municipal Hospital, located in the city of Joinville - Santa Catarina. According to studies by the Global Burden of Disease 2013 (MURRAY et al., 2015), stroke is one of the most important chronic diseases in terms of outreach, being the third major cause of death in Brazil and the leading cause of disability in the world. The proposed systematization seeks to assist researchers and health institutions in the application of the techniques of discovery and analysis of conformity of the processes in the health area, so that such techniques can contribute to the improvement of the flow of activities in health facilities and, consequently, positive effect on health in Brazil. It is formed by nine steps that involve: the knowledge of the Hospital Information System (SIH) and the database; the preparation of the database for application of process mining techniques; the study of care protocols, standard operating procedures, work instructions and other documents made by the health institution for the selected process; the study of clinical guidelines, regulations, norms, laws and other documents that involve the chosen process; the transcription of the documents selected in notation Business Process Management and Notation; the correlation between care protocols, standard operating procedures, work instructions and other documents with clinical guidelines, regulations, standards, laws and other documents; and finally the quantitative analyzes using process mining, such as real process model discovery and compliance analyzes to compare process models with event logs. Keywords: Process Mining. Discovery. Conformance Checker. Systematization. Healthcare. Stroke