Multidisciplinary Collaboration in the Treatment of Patients With Type 2 Diabetes in Primary Care: Analysis Using Process Mining

Process mining to investigate the relationship between clinical antecedents and injury risk, severity and return to play in professional sports

Objective

This paper presents an exploratory case study focusing on the applicability and value of process mining in a professional sports healthcare setting. We explore whether process mining can be retrospectively applied to readily available data at a professional sports club (Football Club Barcelona) and whether it can be used to obtain insights related to care flows.

Design

Our study used discovery process mining to detect patterns and trends in athletes' Post-Pre-Participation Medical Evaluation injury route, encompassing five phases for analysis and interpretation.

Results

We examined preprocessed data in event log format to determine the injury status of athletes in respective baseline groups (healthy or pathological). Our analysis found a link between thigh muscle injuries and later ankle joint problems. The process model found three loops with recurring injuries, the most common of which were thigh muscle injuries. There were no differences in injury rates or the median number of days to return to play between the healthy and pathological groups.

Conclusions

This study explored the applicability and value of process mining in a professional sports healthcare setting. We established that process mining can be retrospectively applied to readily available data at a professional sports club and that this approach can be used to obtain insights related to sports healthcare flows.

Process mining and data mining applications in the domain of chronic diseases: A systematic review

The widespread use of information technology in healthcare leads to extensive data collection, which can be utilised to enhance patient care and manage chronic illnesses. Our objective is to summarise previous studies that have used data mining or process mining methods in the context of chronic diseases in order to identify research trends and future opportunities. The review covers articles that pertain to the application of data mining or process mining methods on chronic diseases that were published between 2000 and 2022. Articles were sourced from PubMed, Web of Science, EMBASE, and Google Scholar based on predetermined inclusion and exclusion criteria. A total of 71 articles met the inclusion criteria and were included in the review. Based on the literature review results, we detected a growing trend in the application of data mining methods in diabetes research. Additionally, a distinct increase in the use of process mining methods to model clinical pathways in cancer research was observed. Frequently, this takes the form of a collaborative integration of process mining, data mining, and traditional statistical methods. In light of this collaborative approach, the meticulous selection of statistical methods based on their underlying assumptions is essential when integrating these traditional methods with process mining and data mining methods. Another notable challenge is the lack of standardised guidelines for reporting process mining studies in the medical field. Furthermore, there is a pressing need to enhance the clinical interpretation of data mining and process mining results.

Impact of student-run clinic participation on empathy and interprofessional skills development in medical and pharmacy students

Background

Students participating in student-run clinics (SRCs) have opportunities to develop and practice beneficial skill sets, including empathy and interprofessional collaboration.

Objectives

This study aimed to assess whether participation in an underserved SRC impacts the development of empathy and interprofessional skills in pharmacy and medical students.

Methods

This study assessed empathy and interprofessional skills development through a self-assessment survey. The survey included the Interpersonal Reactivity Index (IRI) to assess empathy, the Attitudes Towards Health Care Teams/Team Skills Scale (ATHCTS/TSS) to assess interprofessional team dynamics, and a free-text response section. Participants were grouped based on whether they participated in the SRC (intervention group) or did not participate in the SRC (control group). A subgroup analysis was performed based on the participants' discipline (medicine vs. pharmacy). To compare differences in IRI, ATHCTS, and TSS scores between study groups, independent samples t-tests were performed. A thematic analysis was used for qualitative data.

Results

There were no statistically significant differences between intervention and control groups in IRI, ATHCTS, or TSS scores. Subgroup analyses showed no significant differences in scores of student pharmacists or medical students. For both disciplines, the thematic analysis revealed the most common positive themes identified were "real-world patient interaction and care," "impact on practice/career development." Alternatively, it revealed the highest reported negative themes identified as "time management and operational difficulties" and "concerns about the quality of/access to care".

Conclusions

This study demonstrates that involvement in an SRC neither improves nor hinders a learner's development of empathy and interprofessional team skills. Qualitatively, students reported that participation in an SRC benefited their learning and helped develop their skills, like empathy and team dynamics, in an interprofessional setting. Future research with longitudinal monitoring or alternative assessment tools is recommended.

Analytical exploratory tool for healthcare professionals to monitor cancer patients' progress

Introduction

Cancer is a primary public concern in the European continent. Due to the large case numbers and survival rates, a significant population is living with cancer needs. Consequently, health professionals must deal with complex treatment decision-making processes. In this context, a large quantity of data is collected during cancer care delivery. Once collected, these data are complex for health professionals to access to support clinical decision-making and performance review. There is a need for innovative tools that make clinical data more accessible to support cancer health professionals in these activities.

Methods

Following a co-creation, an interactive approach thanks to the Interactive Process Mining paradigm, and data from a tertiary hospital, we developed an exploratory tool to present cancer patients' progress over time.

Results

This work aims to collect and report the process of developing an exploratory analytical Interactive Process Mining tool with clinical relevance for healthcare professionals for monitoring cancer patients' care processes in the context of the LifeChamps project together with a graphical and navigable Process Indicator in the context of prostate cancer patients.

Discussion

The tool presented includes Process Mining techniques to infer actual processes and present understandable results visually and navigable, looking for different types of patients, trajectories, and behaviors.

Characterization of Laboratory Flow and Performance for Process Improvements via Application of Process Mining

BACKGROUND

The rising level of laboratory automation provides an increasing number of logged events that can be used for the characterization of laboratory performance and process improvements. This abundance of data is often underutilized for improving laboratory efficiency.

OBJECTIVES

The first aim of this descriptive study is to provide a structured approach for transforming raw laboratory data to data that is suitable for process mining. The second aim is to describe a process mining approach for mapping and characterizing the sample flow in a clinical chemistry laboratory to identify areas for improvement in the testing process.

METHODS

Data were extracted from instrument log files and the middleware between laboratory instruments and information technology infrastructure. Process mining was used for automated process discovery and analysis. Laboratory performance was quantified in terms of relevant key performance indicators (KPIs): turnaround time, timeliness, workload, work-in-process, and machine downtime.

RESULTS

The method was applied to two Dutch university hospital clinical chemistry laboratories. We identified areas where alternative routes might increase laboratory efficiency and observed the negative effects of machine downtime on laboratory performance. This encourages the laboratory to review sample routes in its analyzer lines, the routes of high priority samples during instrument downtime, as well as the preventive maintenance policy.

CONCLUSION

This article provides the first application of process mining to event data from a medical diagnostic laboratory for automated process model discovery. Our study shows that process mining, with the use of relevant KPIs, provides valuable insights for laboratories that motivates the disclosure and increased utilization of laboratory event data, which in turn drive the analytical staff to intervene in the process to achieve the set performance goals. Our approach is vendor independent and widely applicable for all medical diagnostic laboratories.

Mining context-aware resource profiles in the presence of multitasking

Healthcare organisations are becoming increasingly aware of the need to improve their care processes and to manage their scarce resources efficiently to secure high-quality care standards. As these processes are knowledge-intensive and heavily depend on human resources, a comprehensive understanding of the complex relationship between processes and resources is indispensable for efficient resource management. Organisational mining, a subfield of Process Mining, reveals insights into how (human) resources organise their work based on analysing process execution data recorded in Health Information Systems (HIS). This can be used to, e.g., discover resource profiles which are groups of resources performing similar activity instances, providing an extensive overview of resource behaviour within healthcare organisations. Healthcare managers can employ these insights to allocate their resources efficiently, e.g., by improving the scheduling and staffing of nurses. Existing resource profiling algorithms are limited in their ability to apprehend the complex relationship between processes and resources because they do not take into account the context in which activities were executed, particularly in the context of multitasking. Therefore, this paper introduces ResProMin-MT to discover context-aware resource profiles in the presence of multitasking. In contrast to the state-of-the-art, ResProMin-MT is capable of taking into account more complex contextual activity dimensions, such as activity durations and the degree of multitasking by resources. We demonstrate the feasibility of our method within a real-life healthcare context, validated by medical domain experts.

Using Unified Modeling Language to Analyze Business Processes in the Delivery of Child Health Services

Business Process Management (BPM) has been increasingly used in recent years in the healthcare domain to analyze, optimize, harmonize and compare clinical and healthcare processes. The main aim of this methodology is to model the interactions between medical and organizational activities needed to deliver health services, measure their complexity, variability and deviations to improve the quality of care and its efficiency. Among the different tools, languages and notations developed in the decades, UML (Unified Modeling Language) represents a widely adopted technique to model, analyze and compare business processes in healthcare. We adopted its diagrams in the MOCHA project to compare the different ways of organizing, coordinating and delivering child care across 30 EU/EEA countries both from an organization and control-flow perspectives. This paper provides an overview of the main components used to represent the business process using UML diagrams, also highlighting how we customized them to capture the specificity of the healthcare domain taking into account that processes are reconstructed on the basis of country experts' responses to questionnaires. The benefits of the application of this methodology are demonstrated by providing examples of comparing different aspects of child care.

Health professionals' views and experiences around the dietary and lifestyle management of gestational diabetes in New Zealand

AIM

This study aimed to investigate New Zealand health professionals' views and experiences around the dietary and lifestyle management of gestational diabetes.

METHODS

Semi-structured interviews were conducted remotely with health professionals; sessions were recorded and transcribed. Core themes were extracted using inductive thematic analysis using a framework method.

RESULTS

Twenty-seven health professionals were interviewed (13 diabetes dietitians, 8 specialist diabetes midwives, 2 community midwives, 1 antenatal clinic midwife, 1 obstetrician and 2 endocrinologists). Themes were organised into three central domains: (a) Social and cultural barriers, (b) Service provision and (c) Nutrition advice. Enabling themes included professional collaboration, innovation and creating trusting and supportive environments. Key barriers identified included accessibility, cultural barriers, overwhelmed service, fragmentation and conflicting information and nutrition resource gaps.

CONCLUSIONS

Findings highlight foremost a deficit in primary antenatal nutrition advice that may play a significant role in the fragmentation identified. Investment in community-inclusive services providing antenatal nutrition and diabetes education appears critical to overcome barriers associated with misinformation and poor outcomes. Pathways to include nutrition education from various primary care health providers should be investigated to ease the burden from specialist gestational diabetes clinicians and allow effective delegation of dietetic resources. Revision of current nutrition guidelines for the management of gestational diabetes in New Zealand is needed to facilitate consistent messaging and standards of care.

Clinical and operational insights from data-driven care pathway mapping: a systematic review

Background

Accumulated electronic data from a wide variety of clinical settings has been processed using a range of informatics methods to determine the sequence of care activities experienced by patients. The “as is” or “de facto” care pathways derived can be analysed together with other data to yield clinical and operational information. It seems likely that the needs of both health systems and patients will lead to increasing application of such analyses. A comprehensive review of the literature is presented, with a focus on the study context, types of analysis undertaken, and the utility of the information gained.

Methods

A systematic review was conducted of literature abstracting sequential patient care activities (“de facto” care pathways) from care records. Broad coverage was achieved by initial screening of a Scopus search term, followed by screening of citations (forward snowball) and references (backwards snowball). Previous reviews of related topics were also considered. Studies were initially classified according to the perspective captured in the derived pathways. Concept matrices were then derived, classifying studies according to additional data used and subsequent analysis undertaken, with regard for the clinical domain examined and the knowledge gleaned.

Results

254 publications were identified. The majority (n = 217) of these studies derived care pathways from data of an administrative/clinical type. 80% (n = 173) applied further analytical techniques, while 60% (n = 131) combined care pathways with enhancing data to gain insight into care processes.

Discussion

Classification of the objectives, analyses and complementary data used in data-driven care pathway mapping illustrates areas of greater and lesser focus in the literature. The increasing tendency for these methods to find practical application in service redesign is explored across the variety of contexts and research questions identified. A limitation of our approach is that the topic is broad, limiting discussion of methodological issues.

Conclusion

This review indicates that methods utilising data-driven determination of de facto patient care pathways can provide empirical information relevant to healthcare planning, management, and practice. It is clear that despite the number of publications found the topic reviewed is still in its infancy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12911-022-01756-2.

Impact of pharmacist-involved collaborative care on diabetes management in a primary healthcare setting using real-world data

Background Diabetes mellitus is a complex multi-system disorder, requiring multi-disciplinary care. The conventional care model, where physicians are the sole caregivers may not be optimal. Addition of other healthcare team members improves healthcare outcomes for patients with diabetes. Aim To evaluate the impact of pharmacist-involved collaborative care on diabetes-related outcomes among patients with diabetes attending a primary healthcare setting  in Qatar using real-world data. Method A retrospective cohort study was conducted among patients with diabetes attending Qatar Petroleum Diabetes Clinic. Patients were categorized as either receiving pharmacist-involved collaborative care (intervention group) or usual care (control group). Data were analyzed using SPSS^®. Glycemic control (glycated hemoglobin A₁c, HbA₁c), blood pressure, lipid profile, and body mass index were evaluated at baseline and up to 17 months of follow-up. Results After 17 months of follow-up, pharmacist-involved collaborative care compared to usual care resulted in a significant decrease in HbA₁c (6.8 ± 1.2% vs. 7.1 ± 1.3%, p < 0.01). Moreover, compared to baseline, pharmacist-involved collaborative care significantly improved (p < 0.05) the levels of HbA_1c (7.5% vs. 6.8%), low-density lipoprotein cholesterol (3.7 mmol/L vs. 2.8 mmol/L), total cholesterol (5.43 mmol/L vs. 4.34 mmol/L), and body mass index (30.42 kg/m² vs. 30.17 kg/m²) after 17 months within the intervention group. However, no significant changes for these parameters occurred within the control group. Conclusion The implementation of pharmacist-involved collaborative care in a primary healthcare setting improved several diabetes-related outcomes over 17 months. Future studies should determine the long-term impact of this care model.

Improving the In-Hospital Mortality Prediction of Diabetes ICU Patients Using a Process Mining/Deep Learning Architecture

Diabetes intensive care unit (ICU) patients are at increased risk of complications leading to in-hospital mortality. Assessing the likelihood of death is a challenging and time consuming task due to a large number of influencing factors. Healthcare providers are interested in the detection of ICU patients at higher risk, such that risk factors can possibly be mitigated. While such severity scoring methods exist, they are commonly based on a snapshot of the health conditions of a patient during the ICU stay and do not specifically consider a patient's prior medical history. In this paper, a process mining/deep learning architecture is proposed to improve established severity scoring methods by incorporating the medical history of diabetes patients. First, health records of past hospital encounters are converted to event logs suitable for process mining. The event logs are then used to discover a process model that describes the past hospital encounters of patients. An adaptation of Decay Replay Mining is proposed to combine medical and demographic information with established severity scores to predict the in hospital mortality of diabetes ICU patients. Significant performance improvements are demonstrated compared to established risk severity scoring methods and machine learning approaches using the Medical Information Mart for Intensive Care III dataset.

The interdisciplinary and psychosocial gap in cancer survivorship: A longitudinal study in a Latin American Cancer Center

BACKGROUND AND OBJECTIVES

There is lack of information on the quality of care provided to the rapidly increasing population of cancer survivors in Latin America. Our study attempts to address this gap and to identify areas needed to be improved.

METHODS

A random sample of 210 breast and colorectal cancer survivors were selected from a hospital-based registry in Chile. Cancer registry information, electronic chart review, and personal interviews were used to assess medical and nonmedical care over a 5-year period. Survivorship care practices were compared to a standardized reference based on the US Institute of Medicine domains and the American Cancer Association guidelines.

RESULTS

Over 80% of breast and colorectal cancer survivors received appropriate medical care, ongoing testing surveillance and risk factors assessment. Only a third of survivors were assessed for psychosocial disorders and 25% of them received interdisciplinary care. Overall, 66.1% of breast and 58.6% of colorectal cancer survivors reached the expected quality level of cancer survivorship care according to the reference standard (p < .001).

CONCLUSION

Medical care practices reached a high standard in a leading cancer center in Latin America. However, a much stronger psychosocial assessment and interdisciplinary care is needed to improve survivorship cancer quality care.

Collaborative care model for diabetes in primary care settings in Qatar: a qualitative exploration among healthcare professionals and patients who experienced the service

BACKGROUND

Diabetes mellitus is highly prevalent and associated with huge economic burden globally. The conventional care and management of diabetes mellitus is highly fragmented and complex, warranting the need for a comprehensive Collaborative Care Model (CCM). Little is known about the perception of patients with diabetes and their healthcare providers about CCM, its barriers and facilitators. This study aimed to explore the value of CCM in diabetes care at a primary healthcare (PHC) setting from the perspective of patients with diabetes and healthcare professionals (HCPs), in an effort to expand our current knowledge on collaborative care in diabetes at primary care level for the purpose of quality improvement and service expansion.

METHODS

Using an exploratory case study approach, semi-structured interviews were conducted among patients and HCPs who encountered CCM in Qatar during 2019 and 2020. The semi-structured interviews were transcribed verbatim and the data were analysed and interpreted using a deductive-inductive thematic analysis approach.

RESULTS

Twelve patients and 12 HCPs at a diabetes clinic participated in one-to-one interviews. The interviews resulted in five different themes: the process and components of collaborative care model (four subthemes), current organizational support and resources (three subthemes), impact of collaborative care model on diabetes outcomes (three subthemes), enablers of collaborative care model (three subthemes), and barriers to collaborative care model (three subthemes). The participants indicated easy access to and communication with competent and pleasant HCPs. The patients appreciated the extra time spent with HCPs, frequent follow-up visits, and health education, which empowered them to self-manage diabetes. HCPs believed that successful CCM provision relied on their interest and commitment to care for patients with diabetes. Generally, participants identified barriers and facilitators that are related to patients, HCPs, and healthcare system.

CONCLUSIONS

The providers and users of CCM had an overall positive perception and appreciation of this model in PHC settings. Barriers to CCM such as undesirable attributes of HCPs and patients, unsupportive hospital system, and high workload must be addressed before implementing the model in other PHC settings.

Dynamic Models Supporting Personalised Chronic Disease Management through Healthcare Sensors with Interactive Process Mining

Rich streams of continuous data are available through Smart Sensors representing a unique opportunity to develop and analyse risk models in healthcare and extract knowledge from data. There is a niche for developing new algorithms, and visualisation and decision support tools to assist health professionals in chronic disease management incorporating data generated through smart sensors in a more precise and personalised manner. However, current understanding of risk models relies on static snapshots of health variables or measures, rather than ongoing and dynamic feedback loops of behaviour, considering changes and different states of patients and diseases. The rationale of this work is to introduce a new method for discovering dynamic risk models for chronic diseases, based on patients' dynamic behaviour provided by health sensors, using Process Mining techniques. Results show the viability of this method, three dynamic models have been discovered for the chronic diseases hypertension, obesity, and diabetes, based on the dynamic behaviour of metabolic risk factors associated. This information would support health professionals to translate a one-fits-all current approach to treatments and care, to a personalised medicine strategy, that fits treatments built on patients' unique behaviour thanks to dynamic risk modelling taking advantage of the amount data generated by smart sensors.

Analysis of the relationship between treatment networks and the evolution of patients with Type 2 Diabetes Mellitus

Type 2 Diabetes Mellitus (T2DM) is a chronic disease that has been increasing in prevalence in recent years and that can cause severe complications. To ensure patient care is administered correctly, it is necessary for medical treatment teams to be both multidisciplinary and cohesive. The analysis of health processes is a constant challenge due to their high variability and complexity. This paper proposes a method based on the analysis of social networks to detect treatment networks, and to identify a relationship between these networks and patient evolution, as measured by glycated hemoglobin (HbA1c) levels. The networks were segmented based on patient adherence to their medical appointments and their mean time of delay. We applied this method on a sample of 1574 patients diagnosed with T2DM. Results show that participatory treatment -in which a patient sees a particular group of professionals on a recurrent basis - together with high levels of adherence are associated to those patients who improve their HbA1c levels in the case of high levels of adherence, while those who continually experience referrals to different professionals, remain unstable and, in some cases, get worse. On the other hand, in order to maintain a patient as stable, continuous control of the patient is enough, regardless of the recurrence to the same professionals.

Does a Multidisciplinary Approach to Invasive Breast Cancer Care Improve Time to Treatment and Patient Compliance?

PURPOSE

This study aimed to evaluate whether comprehensive multidisciplinary care (cMDC) for breast cancer patients affected time from diagnosis to treatment, compliance with appointments and to assess for racial disparities.

METHODS

This institutional review board approved retrospective study included adult patients diagnosed with invasive breast cancer between February 2015 and February 2017 and treated at an academic health system where the cMDC program was implemented in February 2016. The cMDC and non-cMDC groups as well as black and white patients were compared to assess time from diagnosis (date of pathology result indicating invasive breast cancer) to treatment (date of surgery or chemotherapy). Compliance was measured by appointments characterized as "no shows" or "canceled due to personal reasons" in the electronic medical record.

RESULTS

Of 541 patients (419 cMDC and 122 non-cMDC), mean time from diagnosis to treatment was significantly longer for blacks than whites in the non-cMDC group (46.9 ± 64.6 days vs 28.2 ± 14.8 days, p = 0.024) and the cMDC group (39.9 ± 34.1 days vs 31.4 ± 16.3 days, p = 0.001). Of 38 (7.2%) patients who started treatment > 60 days after diagnosis, 25 (65.8%) were black. Implementation of cMDC significantly improved patient compliance (missed appointments 4.9 ± 7.6 non-cMDC vs 3.2 ± 4.6 cMDC, p = 0.029).

CONCLUSION

Use of cMDC for invasive breast cancer at our institution highlighted an area for improvement for care administered to blacks and improved patient compliance with appointments.

Towards the Use of Standardized Terms in Clinical Case Studies for Process Mining in Healthcare

Process mining can provide greater insight into medical treatment processes and organizational processes in healthcare. To enhance comparability between processes, the quality of the labelled-data is essential. A literature review of the clinical case studies by Rojas et al. in 2016 identified several common aspects for comparison, which include methodologies, algorithms or techniques, medical fields, and healthcare specialty. However, clinical aspects are not reported in a uniform way and do not follow a standard clinical coding scheme. Further, technical aspects such as details of the event log data are not always described. In this paper, we identified 38 clinically-relevant case studies of process mining in healthcare published from 2016 to 2018 that described the tools, algorithms and techniques utilized, and details on the event log data. We then correlated the clinical aspects of patient encounter environment, clinical specialty and medical diagnoses using the standard clinical coding schemes SNOMED CT and ICD-10. The potential outcomes of adopting a standard approach for describing event log data and classifying medical terminology using standard clinical coding schemes are further discussed. A checklist template for the reporting of case studies is provided in the Appendix A to the article.

Instruments measuring interprofessional collaboration in healthcare - a scoping review

Worldwide there is growing understanding of the importance of interprofessional collaboration in providing well-functioning healthcare. However, little is known about how interprofessional collaboration can be measured between different health-care professionals. In this review, we aim to fill this gap, by identifying and analyzing the existing instruments measuring interprofessional collaboration in healthcare. A scoping review design was applied. A systematic literature search of two electronic databases, Medline (PubMed) and CINAHL, was conducted in 03/2018. The search yielded 1020 studies, of which 35 were selected for the review. The data were analyzed by content analysis. In total, 29 instruments measuring interprofessional collaboration were found. Interprofessional collaboration was measured predominantly between nurses and physicians with different instruments in various health-care settings. Psychometric testing was unsystematic, focusing predominantly on construct and content validity and internal consistency, thus further validation studies with comprehensive testing are suggested. The results of this review can be used to select instruments measuring interprofessional collaboration in practice or research. Future research is needed to strengthen the evidence of reliability and validity of these instruments.

Individual Behavior Modeling with Sensors Using Process Mining

Understanding human behavior can assist in the adoption of satisfactory health interventions and improved care. One of the main problems relies on the definition of human behaviors, as human activities depend on multiple variables and are of dynamic nature. Although smart homes have advanced in the latest years and contributed to unobtrusive human behavior tracking, artificial intelligence has not coped yet with the problem of variability and dynamism of these behaviors. Process mining is an emerging discipline capable of adapting to the nature of high-variate data and extract knowledge to define behavior patterns. In this study, we analyze data from 25 in-house residents acquired with indoor location sensors by means of process mining clustering techniques, which allows obtaining workflows of the human behavior inside the house. Data are clustered by adjusting two variables: the similarity index and the Euclidean distance between workflows. Thereafter, two main models are created: (1) a workflow view to analyze the characteristics of the discovered clusters and the information they reveal about human behavior and (2) a calendar view, in which common behaviors are rendered in the way of a calendar allowing to detect relevant patterns depending on the day of the week and the season of the year. Three representative patients who performed three different behaviors: stable, unstable, and complex behaviors according to the proposed approach are investigated. This approach provides human behavior details in the manner of a workflow model, discovering user paths, frequent transitions between rooms, and the time the user was in each room, in addition to showing the results into the calendar view increases readability and visual attraction of human behaviors, allowing to us detect patterns happening on special days.

Evaluation of multidisciplinary collaboration in pediatric trauma care using EHR data

OBJECTIVES

The study sought to identify collaborative electronic health record (EHR) usage patterns for pediatric trauma patients and determine how the usage patterns are related to patient outcomes.

MATERIALS AND METHODS

A process mining-based network analysis was applied to EHR metadata and trauma registry data for a cohort of pediatric trauma patients with minor injuries at a Level I pediatric trauma center. The EHR metadata were processed into an event log that was segmented based on gaps in the temporal continuity of events. A usage pattern was constructed for each encounter by creating edges among functional roles that were captured within the same event log segment. These patterns were classified into groups using graph kernel and unsupervised spectral clustering methods. Demographics, clinical and network characteristics, and emergency department (ED) length of stay (LOS) of the groups were compared.

RESULTS

Three distinct usage patterns that differed by network density were discovered: fully connected (clique), partially connected, and disconnected (isolated). Compared with the fully connected pattern, encounters with the partially connected pattern had an adjusted median ED LOS that was significantly longer (242.6 [95% confidence interval, 236.9-246.0] minutes vs 295.2 [95% confidence, 289.2-297.8] minutes), more frequently seen among day shift and weekday arrivals, and involved otolaryngology, ophthalmology services, and child life specialists.

DISCUSSION

The clique-like usage pattern was associated with decreased ED LOS for the study cohort, suggesting greater degree of collaboration resulted in shorter stay.

CONCLUSIONS

Further investigation to understand and address causal factors can lead to improvement in multidisciplinary collaboration.

Toward Value-Based Healthcare through Interactive Process Mining in Emergency Rooms: The Stroke Case

The application of Value-based Healthcare requires not only the identification of key processes in the clinical domain but also an adequate analysis of the value chain delivered to the patient. Data Science and Big Data approaches are technologies that enable the creation of accurate systems that model reality. However, classical Data Mining techniques are presented by professionals as black boxes. This evokes a lack of trust in those techniques in the medical domain. Process Mining technologies are human-understandable Data Science tools that can fill this gap to support the application of Value-Based Healthcare in real domains. The aim of this paper is to perform an analysis of the ways in which Process Mining techniques can support health professionals in the application of Value-Based Technologies. For this purpose, we explored these techniques by analyzing emergency processes and applying the critical timing of Stroke treatment and a Question-Driven methodology. To demonstrate the possibilities of Process Mining in the characterization of the emergency process, we used a real log with 9046 emergency episodes from 2145 stroke patients that occurred from January 2010 to June 2017. Our results demonstrate how Process Mining technology can highlight the differences between the flow of stroke patients compared with that of other patients in an emergency. Further, we show that support for health professionals can be provided by improving their understanding of these techniques and enhancing the quality of care.

Team Collaboration Networks and Multidisciplinarity in Diabetes Care: Implications for Patient Outcomes

Prevalence of type 2 diabetes mellitus (T2DM) has almost doubled in recent decades and commonly presents comorbidities and complications. T2DM is a multisystemic disease, requiring multidisciplinary treatment provided by teams working in a coordinated and collaborative manner. The application of social network analysis techniques in the healthcare domain has allowed researchers to analyze interaction between professionals and their roles inside care teams. We studied whether the structure of care teams, modeled as complex social networks, is associated with patient progression. For this, we illustrate a data-driven methodology and use existing social network analysis metrics and metrics proposed for this research. We analyzed appointment and HbA1c blood test result data from patients treated at three primary health care centers, representing six different practices. Patients with good metabolic control during the analyzed period were treated by teams that were more interactive, collaborative and multidisciplinary, whereas patients with worsening or unstable metabolic control were treated by teams with less collaboration and more continuity breakdowns. Results from the proposed metrics were consistent with the previous literature and reveal relevant aspects of collaboration and multidisciplinarity.

Patterns of healthcare seeking behavior among persons with diabetes in Central India: A mixed method study

Background:

Management of diabetes is complex and requires multiple lifestyle modifications, drug therapy, and a sustained regular follow-up. Complexities of health-seeking pattern in individuals with diabetes have been poorly characterized.

Objectives:

To understand the health-seeking patterns, and reasons of provider preference and switching among persons with diabetes.

Materials and Methods:

We performed a mixed methods study in an urban slum setting of Bhopal. This urban slum was chosen as being a field practice area of the institute, a complete sampling frame with listing of households, and individuals with chronic disease conditions (including diabetes) was available. To be included in the study, the individual should have been an adult, aged ≥20 years, and diagnosed as type 2 diabetes mellitus. Descriptive statistical analysis of sociodemographic and disease management variables was performed. For qualitative component, interviews were transcribed and primary coding was done by two investigators followed by condensation of codes into themes or categories. The frequency of these content categories was presented with count and proportions.

Results:

In total, 60 individuals with diabetes were interviewed. Of all individuals, 36 (60%) were asymptomatic at the time of the first diagnosis, and 57 (95%) were currently under treatment from some healthcare provider. About 25 (41.6%) switched their first provider and remaining continued with the same provider. Second provider was sought by 9 (36%) of 25 patients. Reasons for switching were perceived nonrelief, cost of care, distance of facility, and behavior of care provider.

Conclusions:

Healthcare provider switching is common among persons with diabetes which has implications on continuity of care.

Analyzing of Gender Behaviors from Paths Using Process Mining: A Shopping Mall Application

The study presents some results of customer paths' analysis in a shopping mall. Bluetooth-based technology is used to collect data. The event log containing spatiotemporal information is analyzed with process mining. Process mining is a technique that enables one to see the whole process contrary to data-centric methods. The use of process mining can provide a readily-understandable view of the customer paths. We installed iBeacon devices, a Bluetooth-based positioning system, in the shopping mall. During December 2017 and January and February 2018, close to 8000 customer data were captured. We aim to investigate customer behaviors regarding gender by using their paths. We can determine the gender of customers if they go to the men's bathroom or women's bathroom. Since the study has a comprehensive scope, we focused on male and female customers' behaviors. This study shows that male and female customers have different behaviors. Their duration and paths, in general, are not similar. In addition, the study shows that the process mining technique is a viable way to analyze customer behavior using Bluetooth-based technology.

Relationship between Continuity of Care in the Multidisciplinary Treatment of Patients with Diabetes and Their Clinical Results

Multidisciplinary treatment and continuity of care throughout treatment are important for ensuring metabolic control and avoiding complications in diabetic patients. This study examines the relationship between continuity of care of the treating disciplines and clinical evolution of patients. Data from 1836 adult patients experiencing type 2 diabetes mellitus were analyzed, in a period between 12 and 24 months. Continuity was measured by using four well known indices: Usual Provider Continuity (UPC), Continuity of Care Index (COCI), Herfindahl Index (HI), and Sequential Continuity (SECON). Patients were divided into five segments according to metabolic control: well-controlled, worsened, moderately decompensated, highly decompensated, and improved. Well-controlled patients had higher continuity by physicians according to UPC and HI indices (p-values 0.029 and <0.003), whereas highly decompensated patients had less continuity in HI (p-value 0.020). Continuity for nurses was similar, with a greater continuity among well-controlled patients (p-values 0.015 and 0.001 for UPC and HI indices), and less among highly decompensated patients (p-values 0.004 and <0.001 for UPC and HI indices). Improved patients had greater adherence to the protocol than those who worsened. The SECON index showed no significant differences across the disciplines. This study identified a relationship between physicians and nurse’s continuity of care and metabolic control in patients with diabetes, consistent with qualitative findings that highlight the role of nurses in treatment.

Analysis of the Relationship between the Referral and Evolution of Patients with Type 2 Diabetes Mellitus

Type 2 Diabetes Mellitus (T2DM) is a chronic disease that has risen in prominence in recent years and can cause serious complications. Several studies show that the level of adherence to different types of treatment has a direct correlation with the positive evolution of chronic diseases. While such studies relate to patient adherence to medication, those that concern adherence to medical appointments do not distinguish between the different disciplines that attend to or refer patients. This study analyses the relationship between adherence to referrals made by three distinct disciplines (doctors, nurses, and nutritionists) and the results of HbA1c tests from a sample of 2290 patients with T2DM. The aim is to determine whether a relationship exists between patient improvement and the frequency with which they attend scheduled appointments in a timely manner, having been previously referred from or to a particular discipline. Results showed that patients tended to be more adherent when their next appointment is with a doctor, and less adherent when it is with a nurse or nutritionist. Furthermore, patients that remained stable had higher rates of adherence, whereas those with lower adherence tended to be more decompensated. The results can enable healthcare professionals to monitor patients and place particular emphasis on those who do not attend their scheduled appointments in a timely manner.