IT support for healthcare processes – premises, challenges, perspectives

Differential effects of electronic patient record systems for wound care on hospital-acquired pressure injuries: Findings from a secondary analysis of German hospital data

INTRODUCTION

Despite the improvements made in recent decades, the OECD regards hospital-acquired pressure injuries (HAPI) as high priority areas for actions to ensure patient safety. This study was aimed at investigating the degree of utilization of two types of electronic patient record systems for wound care on lowering HAPI rates. Furthermore, the effect of user satisfaction with the systems and perceived alignment with clinical processes should be studied.

MATERIAL AND METHODS

A regression analysis of post-stratified data from German hospitals obtained from the Hospital Quality Reports (observed/expected HAPI ratio) and the IT Report Healthcare was performed. The sample comprised 319 hospitals reporting on digital wound record systems and 199 hospitals on digital nursing record systems for system utilization and the subset of hospitals using a digital system for user satisfaction and process alignment.

RESULTS

The study revealed a significant effect of hospital ownership for both types of systems and a significant interaction of ownership and system utilization for digital wound record systems: Only the for-profit hospitals benefited from a higher degree of system utilization with a lower HAPI ratio. In contrast, non-profit hospitals yielded a reversed pattern, with increasing HAPI rates matching an increased system utilization. User satisfaction (significant) and the perceived alignment of the clinical process (trend) of the digital nursing record system were related with lower HAPI ratios.

DISCUSSION

These findings point to a differential effect of system utilization on HAPI ratios depending on hospital ownership, and they demonstrate that those users who are satisfied with the system can act as catalysts for better care. The explained variance was small but comparable to other studies. Furthermore, it shows that explaining quality care is a complex undertaking. Sheer utilization has no effect while a differential perspective on the facilitators and barriers might help to explain the patient outcomes.

Leveraging process mining for modeling progression trajectories in amyotrophic lateral sclerosis

BACKGROUND

Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease whose spreading and progression mechanisms are still unclear. The ability to predict ALS prognosis would improve the patients' quality of life and support clinicians in planning treatments. In this paper, we investigate ALS evolution trajectories using Process Mining (PM) techniques enriched to both easily mine processes and automatically reveal how the pathways differentiate according to patients' characteristics.

METHODS

We consider data collected in two distinct data sources, namely the Pooled Resource Open-Access ALS Clinical Trials (PRO-ACT) dataset and a real-world clinical register (ALS-BS) including data of patients followed up in two tertiary clinical centers of Brescia (Italy). With a focus on the functional abilities progressively impaired as the disease progresses, we use two Process Discovery methods, namely the Directly-Follows Graph and the CareFlow Miner, to mine the population disease trajectories on the PRO-ACT dataset. We characterize the impairment trajectories in terms of patterns, timing, and probabilities, and investigate the effect of some patients' characteristics at onset on the followed paths. Finally, we perform a comparative study of the impairment trajectories mined in PRO-ACT versus ALS-BS.

RESULTS

We delineate the progression pathways on PRO-ACT, identifying the predominant disabilities at different stages of the disease: for instance, 85% of patients enter the trials without disabilities, and 48% of them experience the impairment of Walking/Self-care abilities first. We then test how a spinal onset increases the risk of experiencing the loss of Walking/Self-care ability as first impairment (52% vs. 27% of patients develop it as the first impairment in the spinal vs. the bulbar cohorts, respectively), as well as how an older age at onset corresponds to a more rapid progression to death. When compared, the PRO-ACT and the ALS-BS patient populations present some similarities in terms of natural progression of the disease, as well as some differences in terms of observed trajectories plausibly due to the trial scheduling and recruitment criteria.

CONCLUSIONS

We exploited PM to provide an overview of the evolution scenarios of an ALS trial population and to preliminary compare it to the progression observed in a clinical cohort. Future work will focus on further improving the understanding of the disease progression mechanisms, by including additional real-world subjects as well as by extending the set of events considered in the impairment trajectories.

Image Watermarking Using Least Significant Bit and Canny Edge Detection

With the advancement in information technology, digital data stealing and duplication have become easier. Over a trillion bytes of data are generated and shared on social media through the internet in a single day, and the authenticity of digital data is currently a major problem. Cryptography and image watermarking are domains that provide multiple security services, such as authenticity, integrity, and privacy. In this paper, a digital image watermarking technique is proposed that employs the least significant bit (LSB) and canny edge detection method. The proposed method provides better security services and it is computationally less expensive, which is the demand of today's world. The major contribution of this method is to find suitable places for watermarking embedding and provides additional watermark security by scrambling the watermark image. A digital image is divided into non-overlapping blocks, and the gradient is calculated for each block. Then convolution masks are applied to find the gradient direction and magnitude, and non-maximum suppression is applied. Finally, LSB is used to embed the watermark in the hysteresis step. Furthermore, additional security is provided by scrambling the watermark signal using our chaotic substitution box. The proposed technique is more secure because of LSB's high payload and watermark embedding feature after a canny edge detection filter. The canny edge gradient direction and magnitude find how many bits will be embedded. To test the performance of the proposed technique, several image processing, and geometrical attacks are performed. The proposed method shows high robustness to image processing and geometrical attacks.

Successful Management of IT Projects in Healthcare Institutions after COVID-19: Role of Digital Orientation and Innovation Adaption

This research aims to examine specific issues that how healthcare institutions successfully manage IT projects after the deadly disease of COVID-19. The world’s healthcare institute changed its traditional way of treatment to IT-based equipment after COVID-19. Hence, this study investigated the how digital orientation helps healthcare institutes for successful management of IT. Our study identifies the critical role of digital orientation and innovation adaption in the successful management of IT. The mediating role of innovation adaption in the association between digital orientation and successful management of IT was also investigated. In total, 456 questionnaires were used for the collection of data from eight different healthcare centers. We selected participants through random sampling. Findings on the healthcare institution showed that successful management of IT is predicted through digital orientation. This study’s results proved that digital orientation impacts innovation adaption, and similarly, innovation adaption influences the successful management of IT. The outcomes show the mediating role of innovation adaption in the linkage between digital orientation and successful management of IT. Current research contributes to the existing literature through combined impacts of the digital orientation, innovation adaption, and successful management of IT through means of demonstrating how, when, and why digital orientation supports the successful management of IT. Moreover, innovation adaption performs a significant role in the extant digitalize world; thus, we chose innovation adaption as a mediator in this study.

Doctor Resistance of Artificial Intelligence in Healthcare

Artificial intelligence (AI) has revolutionized healthcare by enhancing the quality of patient care. Despite its advantages, doctors are still reluctant to use AI in healthcare. Thus, the authors' main objective is to obtain an in-depth understanding of the barriers to doctors' adoption of AI in healthcare. The authors conducted semi-structured interviews with 11 doctors. Thematic analysis as chosen to identify patterns using QSR NVivo (version 12). The results showed that the barriers to AI adoption are lack of financial resources, need for special training, performance risk, perceived cost, technology dependency, need for human interaction, and fear of AI replacing human work.

Information Technology Ambidexterity, Digital Dynamic Capability, and Knowledge Processes as Enablers of Patient Agility: Empirical Study

BACKGROUND

There is a limited understanding of information technology's (IT) role as an enabler of patient agility and the department's ability to respond to patients' needs and wishes adequately.

OBJECTIVE

This study aims to contribute to the insights of the validity of the hypothesized relationship among IT resources, practices and capabilities, and hospital departments' knowledge processes, and the department's ability to adequately sense and respond to patient needs and wishes (ie, patient agility).

METHODS

This study conveniently sampled data from 107 clinical hospital departments in the Netherlands and used structural equation modeling for model assessment.

RESULTS

IT ambidexterity positively enhanced the development of a digital dynamic capability (β=.69; t4999=13.43; P<.001). Likewise, IT ambidexterity also positively impacted the hospital department's knowledge processes (β=.32; t4999=2.85; P=.005). Both digital dynamic capability (β=.36; t4999=3.95; P<.001) and knowledge processes positively influenced patient agility (β=.33; t4999=3.23; P=.001).

CONCLUSIONS

IT ambidexterity promotes taking advantage of IT resources and experiments to reshape patient services and enhance patient agility.

Manufacturing Process of Polymeric Microneedle Sensors for Mass Production

In this work, we present a fabrication process for microneedle sensors made of polylactic acid (PLA), which can be utilized for the electrochemical detection of various biomarkers in interstitial fluid. Microneedles were fabricated by the thermal compression molding of PLA into a laser machined polytetrafluoroethylene (PTFE) mold. Sensor fabrication was completed by forming working, counter, and reference electrodes on each sensor surface by Au sputtering through a stencil mask, followed by laser dicing to separate individual sensors from the substrate. The devised series of processes was designed to be suitable for mass production, where multiple microneedle sensors can be produced at once on a 4-inch wafer. The operational stability of the fabricated sensors was confirmed by linear sweep voltammetry and cyclic voltammetry at the range of working potentials of various biochemical molecules in interstitial fluid.

An information-based framework for development national twin registry: Scoping review and focus group discussion

BACKGROUND

Registries in various clinical domains have been established in the last decades. The specific genetic structure of twins has enabled researchers to find answers to the role of genetics and the environment in medical sciences. Thus, twin registries were developed across the world to support twin studies. Our main objective was to devise a conceptual model for developing the national twin registry to ensure the success of this registry.

METHODS

In this descriptive and qualitative study, the combination of literature review and focus group discussions was applied to achieve suitable models for developing a national twin registry based on lessons learned from founded registries. The qualitative synthesis and reporting results were conducted based on the COREQ checklist.

RESULTS

According to a systematic literature review, the characteristics and employed strategies employed by established twin registries were recognized. Moreover, based on our objectives, suitable models for registry development were defined. The source of information, the different levels of data, and the information flow were determined based on this model.

CONCLUSION

Suggesting a conceptual framework for twin registry development at the national level based on the experiences of other countries could contribute to a greater understanding of twin registry implementation efficiently.

Process mapping in healthcare: a systematic review

INTRODUCTION

Process mapping (PM) supports better understanding of complex systems and adaptation of improvement interventions to their local context. However, there is little research on its use in healthcare. This study (i) proposes a conceptual framework outlining quality criteria to guide the effective implementation, evaluation and reporting of PM in healthcare; (ii) reviews published PM cases to identify context and quality of PM application, and the reported benefits of using PM in healthcare.

METHODS

We developed the conceptual framework by reviewing methodological guidance on PM and empirical literature on its use in healthcare improvement interventions. We conducted a systematic review of empirical literature using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology. Inclusion criteria were: full text empirical study; describing the process through which PM has been applied in a healthcare setting; published in English. Databases searched are: Medline, Embase, HMIC-Health Management Information Consortium, CINAHL-Cumulative Index to Nursing and Allied Health Literature, Scopus. Two independent reviewers extracted and analysed data. Each manuscript underwent line by line coding. The conceptual framework was used to evaluate adherence of empirical studies to the identified PM quality criteria. Context in which PM is used and benefits of using PM were coded using an inductive thematic analysis approach.

RESULTS

The framework outlines quality criteria for each PM phase: (i) preparation, planning and process identification, (ii) data and information gathering, (iii) process map generation, (iv) analysis, (v) taking it forward. PM is used in a variety of settings and approaches to improvement. None of the reviewed studies (N = 105) met all ten quality criteria; 7% were compliant with 8/10 or 9/10 criteria. 45% of studies reported that PM was generated through multi-professional meetings and 15% reported patient involvement. Studies highlighted the value of PM in navigating the complexity characterising healthcare improvement interventions.

CONCLUSION

The full potential of PM is inhibited by variance in reporting and poor adherence to underpinning principles. Greater rigour in the application of the method is required. We encourage the use and further development of the proposed framework to support training, application and reporting of PM.

TRIAL REGISTRATION

Prospero ID: CRD42017082140.

Driving Digital Transformation During a Pandemic: Case Study of Virtual Collaboration in a German Hospital

Background

The COVID-19 pandemic has not only changed the private lives of millions of people but has significantly affected the collaboration of medical specialists throughout health care systems worldwide. Hospitals are making changes to their regular operations to slow the spread of SARS-CoV-2 while ensuring the treatment of emergency patients. These substantial changes affect the typical work setting of clinicians and require the implementation of organizational arrangements.

Objective

In this study, we aim to increase our understanding of how digital transformation drives virtual collaboration among clinicians in hospitals in times of crisis, such as the COVID-19 pandemic.

Methods

We present the lessons learned from an exploratory case study in which we observed the introduction of an information technology (IT) system for enhancing collaboration among clinicians in a German hospital. The results are based on 16 semistructured interviews with physicians from various departments and disciplines; the interviews were generalized to better understand and interpret the meaning of the statements.

Results

Three key lessons and recommendations explain how digital transformation ensures goal-driven collaboration among clinicians. First, we found that implementing a disruptive change requires alignment of the mindsets of the stakeholders. Second, IT-enabled collaboration presupposes behavioral rules that must be followed. Third, transforming antiquated processes demands a suitable technological infrastructure.

Conclusions

Digital transformation is being driven by the COVID-19 pandemic. However, the rapid introduction of IT-enabled collaboration reveals grievances concerning the digital dissemination of medical information along the patient treatment path. To avoid being caught unprepared by future crises, digital transformation must be further driven to ensure collaboration, and the diagnostic and therapeutic process must be opened to disruptive strategies.

Overcoming Resistance to Big Data and Operational Changes Through Interactive Data Visualization

Research has shown that the use of big data can modify operational processes in organizations. However, little research has been conducted on overcoming resistance to the process changes needed for adoption of big data technologies. In this article, we address this gap in the literature by investigating the impact of interactive data visualization on decision-making around operational process changes with big data. Our goal is to demonstrate how the choice of visualization of workflow and operational processes impacts decisions to embrace real-time, big data technology. To do so, we conduct a case study of patient/provider interactions in a large health care practice and compare the initial state with a revised workflow using a big data, real-time analytics platform. We then investigate the impact of the data visualization strategy on decision-making to implement operational changes caused by big data. The study demonstrates that interactive data visualization of operational processes can be an enabler in overcoming organizational resistance to big data technologies in a change-resistant organization. The concomitant benefit is that big data analytics is placed directly into the hands of primary decision makers.

Automatic and Explainable Labeling of Medical Event Logs With Autoencoding

Process mining is a suitable method for knowledge extraction from patient pathways. Structured in event logs, medical events are complex, often described using various medical codes. An efficient labeling of these events before applying process mining analysis is challenging. This paper presents an innovative methodology to handle the complexity of events in medical event logs. Based on autoencoding, accurate labels are created by clustering similar events in latent space. Moreover, the explanation of created labels is provided by the decoding of its corresponding events. Tested on synthetic events, the method is able to find hidden clusters on sparse binary data, as well as accurately explain created labels. A case study on real healthcare data is performed. Results confirm the suitability of the method to extract knowledge from complex event logs representing patient pathways.

Recommendations for enhancing the usability and understandability of process mining in healthcare

Healthcare organizations are confronted with challenges including the contention between tightening budgets and increased care needs. In the light of these challenges, they are becoming increasingly aware of the need to improve their processes to ensure quality of care for patients. To identify process improvement opportunities, a thorough process analysis is required, which can be based on real-life process execution data captured by health information systems. Process mining is a research field that focuses on the development of techniques to extract process-related insights from process execution data, providing valuable and previously unknown information to instigate evidence-based process improvement in healthcare. However, despite the potential of process mining, its uptake in healthcare organizations outside case studies in a research context is rather limited. This observation was the starting point for an international brainstorm seminar. Based on the seminar's outcomes and with the ambition to stimulate a more widespread use of process mining in healthcare, this paper formulates recommendations to enhance the usability and understandability of process mining in healthcare. These recommendations are mainly targeted towards process mining researchers and the community to consider when developing a new research agenda for process mining in healthcare. Moreover, a limited number of recommendations are directed towards healthcare organizations and health information systems vendors, when shaping an environment to enable the continuous use of process mining.

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.

Process-Aware Enactment of Clinical Guidelines through Multimodal Interfaces

Healthcare is one of the largest business segments in the world and is a critical area for future growth. In order to ensure efficient access to medical and patient-related information, hospitals have invested heavily in improving clinical mobile technologies and spreading their use among doctors towards a more efficient and personalized delivery of care procedures. However, there are also indications that their use may have a negative impact on patient-centeredness and often places many cognitive and physical demands on doctors, making them prone to make medical errors. To tackle this issue, in this paper, we present the main outcomes of the project TESTMED, which aimed at realizing a clinical system that provides operational support to doctors using mobile technologies for delivering care to patients, in a bid to minimize medical errors. The system exploits concepts from Business Process Management (BPM) on how to manage a specific class of care procedures, called clinical guidelines, and how to support their execution and mobile orchestration among doctors. To allow a non-invasive interaction of doctors with the system, we leverage the use of touch and vocal user interfaces. A robust user evaluation performed in a real clinical case study shows the usability and effectiveness of the system.

Temporal variability analysis reveals biases in electronic health records due to hospital process reengineering interventions over seven years

OBJECTIVE

To evaluate the effects of Process-Reengineering interventions on the Electronic Health Records (EHR) of a hospital over 7 years.

MATERIALS AND METHODS

Temporal Variability Assessment (TVA) based on probabilistic data quality assessment was applied to the historic monthly-batched admission data of Hospital La Fe Valencia, Spain from 2010 to 2016. Routine healthcare data with a complete EHR was expanded by processed variables such as the Charlson Comorbidity Index.

RESULTS

Four Process-Reengineering interventions were detected by quantifiable effects on the EHR: (1) the hospital relocation in 2011 involved progressive reduction of admissions during the next four months, (2) the hospital services re-configuration incremented the number of inter-services transfers, (3) the care-services re-distribution led to transfers between facilities (4) the assignment to the hospital of a new area with 80,000 patients in 2015 inspired the discharge to home for follow up and the update of the pre-surgery planned admissions protocol that produced a significant decrease of the patient length of stay.

DISCUSSION

TVA provides an indicator of the effect of process re-engineering interventions on healthcare practice. Evaluating the effect of facilities' relocation and increment of citizens (findings 1, 3-4), the impact of strategies (findings 2-3), and gradual changes in protocols (finding 4) may help on the hospital management by optimizing interventions based on their effect on EHRs or on data reuse.

CONCLUSIONS

The effects on hospitals EHR due to process re-engineering interventions can be evaluated using the TVA methodology. Being aware of conditioned variations in EHR is of the utmost importance for the reliable reuse of routine hospitalization data.

Object-Specific Role-Based Access Control

The proper management of privacy and security constraints in information systems in general and access control in particular constitutes a tremendous, but still prevalent challenge. Role-based access control (RBAC) and its variations can be considered as the widely adopted approach to realize authorization in information systems. However, RBAC lacks a proper object-specific support, which disallows establishing the fine-grained access control required in many domains. By comparison, attribute-based access control (ABAC) enables a fine-grained access control based on policies and rules evaluating attributes. As a drawback, ABAC lacks the abstraction of roles. Moreover, it is challenging to engineer and to audit the granted privileges encoded in rule-based policies. This paper presents the generic approach of object-specific role-based access control (ORAC). On one hand, ORAC enables information system engineers, administrators and users to utilize the well-known principle of roles. On the other hand, ORAC allows realizing the access to objects in a fine-grained way where required. The approach was systematically established according to well-elicited key requirements for fine-grained access control in information systems. For the purpose of evaluation, the approach was applied to real-world scenarios and implemented in a proof-of-concept prototype demonstrating its feasibility and applicability.

Fostering a healthcare sector quality and safety culture

PURPOSE

Quality issues, increasing patient expectations and unsatisfactory media reports are driving patient safety concerns. Developing a quality and safety culture (QSC) is, therefore, crucial for patient and staff welfare, and should be a priority for service providers and policy makers. The purpose of this paper is to identify the most important QSC drivers, and thus propose appropriate operational actions for Saudi Arabian hospital managers and for managers in healthcare institutions worldwide.

DESIGN/METHODOLOGY/APPROACH

Quantitative data from 417 questionnaires were analyzed using structural equation modeling. Respondents were selected from various hospitals and managerial positions at a national level.

FINDINGS

Findings suggest that error feedback (FAE) and communication quality (QC) have a strong role fostering or enhancing QSC. Findings also show that fearing potential punitive responses to mistakes made on the job, hospital staff are reluctant to report errors.

PRACTICAL IMPLICATIONS

To achieve a healthcare QSC, managers need to implement preemptive or corrective actions aimed at ensuring prompt and relevant feedback about errors, ensure clear and open communication and focus on continuously improving systems and processes rather than on failures related to individual performance.

ORIGINALITY/VALUE

This paper adds value to national healthcare, as Saudi study results are probably generalizable to other healthcare systems throughout the world.

Computerized Systems Supporting Clinical Decision in Medicine

Statistics is the science of collection, summarizing, presentation and interpretation of data. Moreover, it yields methods used in the verification of research hypotheses. The presence of a statistician in a research group remarkably improves both the quality of design and research and the optimization of financial resources. Moreover, the involvement of a statistician in a research team helps the physician to effectively utilize the time and energy spent on diagnosing, which is an important aspect in view of limited healthcare resources. Precise, properly designed and implemented Computerized Clinical Decision Support Systems certainly lead to the improvement of healthcare and the quality of medical services, which increases patient satisfaction and reduces financial burdens on healthcare systems.

The assessment of data quality issues for process mining in healthcare using Medical Information Mart for Intensive Care III, a freely available e-health record database

There is a growing body of literature on process mining in healthcare. Process mining of electronic health record systems could give benefit into better understanding of the actual processes happened in the patient treatment, from the event log of the hospital information system. Researchers report issues of data access approval, anonymisation constraints, and data quality. One solution to progress methodology development is to use a high-quality, freely available research dataset such as Medical Information Mart for Intensive Care III, a critical care database which contains the records of 46,520 intensive care unit patients over 12 years. Our article aims to (1) explore data quality issues for healthcare process mining using Medical Information Mart for Intensive Care III, (2) provide a structured assessment of Medical Information Mart for Intensive Care III data quality and challenge for process mining, and (3) provide a worked example of cancer treatment as a case study of process mining using Medical Information Mart for Intensive Care III to illustrate an approach and solution to data quality challenges. The electronic health record software was upgraded partway through the period over which data was collected and we use this event to explore the link between electronic health record system design and resulting process models.

Probabilistic modeling personalized treatment pathways using electronic health records

BACKGROUND

Modeling personalized treatment pathways plays an important role in understanding essential/critical treatment behaviors performed on patients during their hospitalizations and thus provides the opportunity for the improvement of better health service delivery in treatment pathways.

OBJECTIVE

Unlike traditional business process mining, modeling personalized treatment pathways is more challenging because they are typically case-specific. Although several studies have been devoted to modeling patient treatment pathways, limited efforts have been made on the extraction of latent semantics and their transitions behind patient treatment pathways, which are often ambiguous and poorly understood.

METHODS

In this article, we propose an extension of the Hidden Markov Model to mine and model personalized treatment pathways by extracting latent treatment topics and identifying their sequential dependencies in pathways, in the form of probabilistic distributions and transitions of patients' raw Electronic Health Record (EHR) data.

RESULTS

We evaluated the proposed model on 48,024 patients with cardiovascular diseases. A total of 15 treatment topics and their typical transition routes were discovered from EHR data that contained 1,391,251 treatment events with 2786 types of interventions and that were evaluated by ten clinicians manually. The obtained p-values are 0.000146 and 0.009106 in comparison with both Latent Dirichlet Allocation and Sequent Naïve Bayes models, respectively; this outcome indicate that our approach achieves a better understanding of human evaluators on modeling personalized treatment pathway than that of benchmark models.

CONCLUSION

The experimental results on a real-world data set clearly suggest that the proposed model has efficiency in mining and modeling personalized treatment pathways. We argue that the discovered treatment topics and their transition routes, as actionable knowledge that represents the practice of treating individual patients in their clinical pathways, can be further exploited to help physicians better understand their specialty and learn from previous experiences for treatment analysis and improvement.

Information Technology in Healthcare: HHC-MOTES, a Novel Set of Metrics to Analyse IT Sustainability in Different Areas

Sustainability, as a science, is the guideline of the present work. It aims to analyse, by means of a literature review, various areas of healthcare in which information technology (IT) has been- or could be-used, leading to several sources of sustainability, for example, cost savings, better teamwork, higher quality and efficiency of medical care. After a brief introduction analysing the strategic contexts in which innovation in general, and IT in particular, can be a source of general improvements in efficiency, cost savings and service quality, the research focuses on the healthcare system by discussing the different nature of private and public organizations in terms of adopting innovations and changes and discussing the issue of consumer health costs and consumer choices. The following part focuses on the qualitative benefits of IT in healthcare and discusses the importance of metrics for measuring performance, costs and efficiency in this area. The work then qualitatively introduces a new set of Key Performance Indicators (KPI), partly based on literature from different topics and existing and validated sets of metrics, analysing, under the point of view of sustainability, the implementation of IT in healthcare, namely in management, organization, technology, environment and social fields (HHC-MOTES framework). The model, inspired by and to sustainability, can be used as a decision support at the strategic management level as well as for the analysis and investigation of the effects of IT systems in the healthcare sector from various perspectives.

Formalization and acquisition of temporal knowledge for decision support in medical processes

BACKGROUND

In medical practice, long term interventions are common and they require timely planning of the involved processes. Unfortunately, evidence-based statements about time are hard to find in Clinical Practice Guidelines (CPGs) and in other sources of medical knowledge. At the same time, health care centers use medical records and information systems to register data about clinical processes and patients, including time information about the encounters, prescriptions, and other clinical actions. Consequently, medical records and health care information systems are promising sources of data from which we can detect temporal medical knowledge.

OBJECTIVE

The objectives were to (1) Analyze and classify the sorts of time constraints in medical processes, (2) Propose a formalism to represent these sorts of clinical time constraints, (3) Use these formalisms to enable the automatic generation of temporal models from clinical data, and (4) Study the adherence of these intervention models to CPG recommendations.

METHODS

In order to achieve these objectives, we carried out four studies: The identification of the sort of times involved in the long-term diagnostic and therapeutic medical procedures of fifty patients, the supervision of the indications about time contained in six CPGs on chronic diseases, the study of the time structures of two standard data models, as well as ten languages to computerize CPGs. Based on the provided studies, we synthesized two representation formalisms: Micro- and macro-temporality. We developed three algorithms for automatic generation of generalized time constraints in the form of micro- and macro-temporalities from clinical databases, which were double tested.

RESULTS

A full classification of time constraints for medical procedures is proposed. Two formalisms called micro- and macro-temporality are introduced and validated to represent these time constraints. Time constraints were generated automatically from the data about 8781 Arterial Hypertension (AH) patients. The generated macro-temporalities restricted visits to be between 1-7 weeks, whereas CPGs recommend 2-4 weeks. Micro-temporal constraints on drug-dosage therapies distinguished between the initial dosage and the target dosage, with visits every 1-6 weeks, and 2-5 months, respectively. Our algorithms obtained semi-complete maps of dosage increments and the maximum dosages for 7 drug types. Data-based time limits for lifestyle change counsels and blood pressure (BP) check-ups were fixed to 6 and 3 months, for patients with low- and high-BP, respectively, when CPGs specify a general 3-6 month range.

CONCLUSIONS

Experience-based temporal knowledge detected using our algorithms complements the evidence-based knowledge about clinical procedures contained in the CPGs. Our temporal model is simple and highly descriptive when dealing with general or specific time constraints' representations, offering temporal knowledge representation of varying detail. Therefore, it is capable of capturing all the temporal knowledge we can find in medical procedures, when dealing with chronic diseases. With our model and algorithms, an adherence analysis emerges naturally to detect CPG-compliant interventions, but also deviations whose causes and possible rationales can call into question CPG recommendations (e.g., our analysis of AH patients showed that the time between visits recommended by CPGs were too long for a proper drug therapy decision, dosage titration, or general follow-up).

The use of process mapping in healthcare quality improvement projects

Introduction Process mapping provides insight into systems and processes in which improvement interventions are introduced and is seen as useful in healthcare quality improvement projects. There is little empirical evidence on the use of process mapping in healthcare practice. This study advances understanding of the benefits and success factors of process mapping within quality improvement projects. Methods Eight quality improvement projects were purposively selected from different healthcare settings within the UK's National Health Service. Data were gathered from multiple data-sources, including interviews exploring participants' experience of using process mapping in their projects and perceptions of benefits and challenges related to its use. These were analysed using inductive analysis. Results Eight key benefits related to process mapping use were reported by participants (gathering a shared understanding of the reality; identifying improvement opportunities; engaging stakeholders in the project; defining project's objectives; monitoring project progress; learning; increased empathy; simplicity of the method) and five factors related to successful process mapping exercises (simple and appropriate visual representation, information gathered from multiple stakeholders, facilitator's experience and soft skills, basic training, iterative use of process mapping throughout the project). Conclusions Findings highlight benefits and versatility of process mapping and provide practical suggestions to improve its use in practice.

A two-step approach for mining patient treatment pathways in administrative healthcare databases

Clustering electronic medical records allows the discovery of information on healthcare practices. Entries in such medical records are usually composed of a succession of diagnostics or therapeutic steps. The corresponding processes are complex and heterogeneous since they depend on medical knowledge integrating clinical guidelines, the physician's individual experience, and patient data and conditions. To analyze such data, we are first proposing to cluster medical visits, consultations, and hospital stays into homogeneous groups, and then to construct higher-level patient treatment pathways over these different groups. These pathways are then also clustered to distill typical pathways, enabling interpretation of clusters by experts. This approach is evaluated on a real-world administrative database of elderly people in Québec suffering from heart failures.

ProFUSO: Business process and ontology-based framework to develop ubiquitous computing support systems for chronic patients' management

New advances in telemedicine, ubiquitous computing, and artificial intelligence have supported the emergence of more advanced applications and support systems for chronic patients. This trend addresses the important problem of chronic illnesses, highlighted by multiple international organizations as a core issue in future healthcare. Despite the myriad of exciting new developments, each application and system is designed and implemented for specific purposes and lacks the flexibility to support different healthcare concerns. Some of the known problems of such developments are the integration issues between applications and existing healthcare systems, the reusability of technical knowledge in the creation of new and more sophisticated systems and the usage of data gathered from multiple sources in the generation of new knowledge. This paper proposes a framework for the development of chronic disease support systems and applications as an answer to these shortcomings. Through this framework our pursuit is to create a common ground methodology upon which new developments can be created and easily integrated to provide better support to chronic patients, medical staff and other relevant participants. General requirements are inferred for any support system from the primary attention process of chronic patients by the Business Process Management Notation. Numerous technical approaches are proposed to design a general architecture that considers the medical organizational requirements in the treatment of a patient. A framework is presented for any application in support of chronic patients and evaluated by a case study to test the applicability and pertinence of the solution.

Waiting too long: bottlenecks and improvements – a case study of a surgery department

Purpose

The purpose of this paper is to promote a business process approach for developing and improving the efficiency of hospital service quality in order to reduce clinical risks and increase patients satisfaction. The problems healthcare facilities face are how to reduce waste and risk and improve quality. The adoption of a process-focused organization could reduce organizational errors which have a negative influence on performance.

Design/methodology/approach

The research is based on a case study methodology, analyzing a specific real-life case. It is shown a practical example in the surgery department of an Italian hospital, identifying, analyzing and managing critical situations, in terms of improvement. To this end, national and international contributions, public documents, institutional websites, conference papers, books, workshops and hospital websites have been analyzed. Moreover, other data were collected through questionnaires.

Findings

Results show how a comprehensive view of the processes may lead to improvement in operations by identifying different risks and bottlenecks, suggesting the rapid implementation of corrective policies and improvements, in terms of overall efficiency.

Practical implications

By implementing innovative organizational processes to identify and reduce bottleneck a healthcare system could achieve a competitive advantage.

Originality/value

Within the Italian healthcare system, limited attention has been paid to the design of healthcare facilities. Flexible solutions are necessary for lean management. The originality of this work lies in the analysis applied to a complex organization, through which hidden critical situations have been identified, and solutions to improve and provide better healthcare management have been suggested.

A Methodological Framework for the Integrated Design of Decision-Intensive Care Pathways-an Application to the Management of COPD Patients

Healthcare processes are by nature complex, mostly due to their multidisciplinary character that requires continuous coordination between care providers. They encompass both organizational and clinical tasks, the latter ones driven by medical knowledge, which is inherently incomplete and distributed among people having different expertise and roles. Care pathways refer to planning and coordination of care processes related to specific groups of patients in a given setting. The goal in defining and following care pathways is to improve the quality of care in terms of patient satisfaction, costs reduction, and medical outcome. Thus, care pathways are a promising methodological tool for standardizing care and decision-making. Business process management techniques can successfully be used for representing organizational aspects of care pathways in a standard, readable, and accessible way, while supporting process development, analysis, and re-engineering. In this paper, we introduce a methodological framework that fosters the integrated design, implementation, and enactment of care processes and related decisions, while considering proper representation and management of organizational and clinical information. We focus here and discuss in detail the design phase, which encompasses the simulation of care pathways. We show how business process model and notation (BPMN) and decision model and notation (DMN) can be combined for supporting intertwined aspects of decision-intensive care pathways. As a proof-of-concept, the proposed methodology has been applied to design care pathways related to chronic obstructive pulmonary disease (COPD) in the region of Veneto, in Italy.

Discovering health-care processes using DeciClareMiner

Flexible, human-centric and knowledge-intensive processes occur in many service industries and are prominent in the health-care sector. Knowledge workers (e.g., doctors or other health-care personnel) are given the flexibility to address each process instance (i.e., episode of care) in the way that they deem most suitable. As a result, the knowledge of these processes is generally of a tacit nature, with many stakeholders lacking a clear view of a process. In this paper, we propose an algorithm called DeciClareMiner that combines process and decision mining to extract a process model and the corresponding knowledge from past executions of these processes. The algorithm was evaluated by applying it to a realistic health-care case and comparing the results to a complete search benchmark. In a relatively short time (10 min), DeciClareMiner was able to produce a DeciClare model that represents 93% of episodes of care with atomic constraints. Compared to the 50 h required to calculate the 100%-episode model via an exhaustive search approach, our result is considered a major improvement.

Supporting breast cancer decisions using formalized guidelines and experts decision patterns: initial prototype and evaluation

Transparent decisions and its documentation of breast cancer patients' therapy are getting more important especially since modern therapeutic approaches favor personalized forms of treatment. The medical decisions for a treatment are very complex, because there are rules and different options for each patient. To support the decision process, we analyzed the current decision rules and implemented them in a prototype of a rule-based expert system. Thus, this system shall support the quality assurance regarding transparent documentation of individualized therapeutic decisions. For evaluating the system, we used data from a state tumor center and compared the decisions suggested by our system with expert ones. The system and the expert approach will be compared with each other as well as the differences in the treatment decisions. The first preliminary results show us that the human factor-like must be considered by creating a decision support system. The prototype delivers first results, which are restricted, but the results are promising for further developments.

Empowerment of Patients with Hypertension through BPM, IoT and Remote Sensing

Hypertension affects one in five adults worldwide. Healthcare processes require interdisciplinary cooperation and coordination between medical teams, clinical processes, and patients. The lack of patients’ empowerment and adherence to treatment makes necessary to integrate patients, data collecting devices and clinical processes. For this reason, in this paper we propose a model based on Business Process Management paradigm, together with a group of technologies, techniques and IT principles which increase the benefits of the paradigm. To achieve the proposed model, the clinical process of the hypertension is analyzed with the objective of detecting weaknesses and improving the process. Once the process is analyzed, an architecture that joins health devices and environmental sensors, together with an information system, has been developed. To test the architecture, a web system connected with health monitors and environment sensors, and with a mobile app have been implemented.

Computer technologies to integrate medical treatments to manage multimorbidity

The high prevalence of multimorbid cases is a challenge for Health-Care Systems today. Clinical practice guidelines are the means to register and transmit the available evidence-based medical knowledge concerning concrete diseases. Several computer languages have been defined to represent this knowledge in a way that computers could use to help physicians in the daily practice of medicine. The generation of guidelines for all possible multimorbidities entails several issues that are difficult to address. Consequently, numerous medical informatics technologies have appeared merging computer information structures in a way that the treatment knowledge about single diseases could be combined in order to deliver health-care to patients suffering from multimorbidity. This paper proposes a classification of the most promising current technologies addressing this issue and provides an analysis of their maturity, strengths, and weaknesses. We conclude with an enumeration of ten relevant issues to consider when developing such technologies.

Intelligent Process Adaptation in the SmartPM System

The increasing application of process-oriented approaches in new challenging dynamic domains beyond business computing (e.g., healthcare, emergency management, factories of the future, home automation, etc.) has led to reconsider the level of flexibility and support required to manage complex knowledge-intensive processes in such domains. A knowledge-intensive process is influenced by user decision making and coupled with contextual data and knowledge production, and involves performing complex tasks in the “physical” real world to achieve a common goal. The physical world, however, is not entirely predictable, and knowledge-intensive processes must be robust to unexpected conditions and adaptable to unanticipated exceptions, recognizing that in real-world environments it is not adequate to assume that all possible recovery activities can be predefined for dealing with the exceptions that can ensue. To tackle this issue, in this paper we present SmartPM, a model and a prototype Process Management System featuring a set of techniques providing support for automated adaptation of knowledge-intensive processes at runtime. Such techniques are able to automatically adapt process instances when unanticipated exceptions occur, without explicitly defining policies to recover from exceptions and without the intervention of domain experts at runtime, aiming at reducing error-prone and costly manual ad-hoc changes, and thus at relieving users from complex adaptations tasks. To accomplish this, we make use of well-established techniques and frameworks from Artificial Intelligence, such as situation calculus, IndiGolog and classical planning. The approach, which is backed by a formal model, has been implemented and validated with a case study based on real knowledge-intensive processes coming from an emergency management domain.

A process mining-based investigation of adverse events in care processes

This paper proposes the Clinical Pathway Analysis Method (CPAM) approach that enables the extraction of valuable organisational and medical information on past clinical pathway executions from the event logs of healthcare information systems. The method deals with the complexity of real-world clinical pathways by introducing a perspective-based segmentation of the date-stamped event log. CPAM enables the clinical pathway analyst to effectively and efficiently acquire a profound insight into the clinical pathways. By comparing the specific medical conditions of patients with the factors used for characterising the different clinical pathway variants, the medical expert can identify the best therapeutic option. Process mining-based analytics enables the acquisition of valuable insights into clinical pathways, based on the complete audit traces of previous clinical pathway instances. Additionally, the methodology is suited to assess guideline compliance and analyse adverse events. Finally, the methodology provides support for eliciting tacit knowledge and providing treatment selection assistance.

Robust Execution of Mobile Activities in Process-Aware Information Systems

Process management technology constitutes a crucial component of service-oriented environments as it facilitates the composition of services at design time and their orchestration at run time. In this context, high flexibility is required as business functions must be quickly adaptable to cope with dynamic changes in the business. The tremendous proliferation of smart mobile devices over the last years has fostered their prevalence in knowledge-intensive areas. As a result, it is frequently demanded to enhance process-aware information systems with mobile activity support. The latter constitutes process activities (i.e., single process steps) to be executed on smart mobile devices. In general, the technical integration of this activity type with existing process management technology is challenging. If a mobile context shall be additionally considered when executing the activities, the integration gets even more complex. However, the use of such a mobile context offers several advantages. For example, (mobile) activity execution time can be significantly decreased if mobile activities are only assigned to those users whose location is close to the one of the mobile activity. Existing research approaches mainly focus on the partitioning of processes and the distributed execution of the resulting fragments on smart mobile devices. Opposed to this fragmentation concept, this paper proposes an approach to enable the robust and flexible execution of single process activities on smart mobile devices.