VALUE solution is based on PM4H for applying IPM analysis in clinical and health care settings. Such a solution can be directly used as a stand-alone toolkit or integrated within organizations premises and their systems through standards as a full software suite.

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The burden of longer life expectancy with a prevalence of chronic diseases and multimorbidity on the rise is becoming unsustainable for public healthcare. The problem is systemic and risks the sustainability of the current healthcare model in Europe. Digital health transformation together with Value-Based Healthcare could ease the burden pressure improving protocols and pathways and making them more efficient.

Unfortunately, the adoption of digital tools, like AI, remains poor, failing to breach acceptance barriers. Interactive Process Mining (IPM) is the best Machine Learning approach to study clinical pathways and patient care flows for supporting healthcare improvement because it is a methodology, specifically conceived to involve the clinical expert in the production of the AI models, incorporating their medical and clinical knowledge in building and interpreting the models. VALUE will provide a full interoperable Interactive Process Mining tool, offering option services to healthcare organizations.

Clinicians are continuously optimizing the workflow using clinical and patient-reported outcomes. IT experts and clinicians do not always speak the same language, and to do this optimization and sometimes redesign, process mining is a key element. Unfortunately, the adoption of digital tools and AI remains poor, failing to breach acceptance barriers existing in a work environment strongly driven by evidence and understanding.

The problem is systemic and risks the sustainability of the current healthcare model in Europe. According to the World Health Organization (WHO), in 2025, there will be 1.200 million people over 60 in the world, where approximately 75% of them will live in Europe. Over the next 15 years, people will suffer more death and disability from non-communicable diseases which accounts for an estimated 77% of the disease burden and 86% of the deaths in the European Region.

One of the solutions for this burden is an effective adoption of value-based healthcare, redefining clinical workflows based on process mining.

The need for protocol assessments tools is a system-level problem that affects to all the agents in the healthcare system, as was idenfied in previous work from consortium partners:

  • Hospital managers: with the need to improve and redefine healthcare processes without reducing the quality of the health system
  • Clinical staff: cannot have a proper understanding of multi-disease treatment protocols interactions.
  • Patients and families: cannot obtain more efficient healthcare that improves their quality perception
  • Insurers: cannot offer competitive solutions.

For the VALUE solution, the key influential stakeholders are Hospital managers and Clinician staff at first stages since they have the final decision for the adoption of new tools.

VALUE solution is a software tool for applying IPM analysis in clinical and health care settings. Such a tool can be directly used as a stand-alone toolkit or integrated within organization premises and their systems through standards as a full software suite. Through a validated methodology to train healthcare professionals (see PATHWAYS 2019), the solution will be systematically deployed, following an iterative method, at different clinical and hospital scenarios identifying bottlenecks, finding best clinical pathways available in each one of the scenarios where commercial viability has been already assessed: emergency care flow, cardiac disease and patient management, and pain management.

  • A. Martinez-Millana, G. Fico, C. Fernandez-Llatas and .. Traver, Performance assessment of a closed-loop system for diabetes management, Medical \& Biological Engineering \& Computing, vol.2015, pp.1--9, 2015.
  • Telemedicine systems can play an important role in the management of diabetes, a chronic condition that is increasing worldwide. Evaluations on the consistency of information across these systems and on their performance in a real situation are still missing. This paper presents a remote monitoring system for diabetes management based on physiological sensors, mobile technologies and patient/doctor applications over a service-oriented architecture that has been evaluated in an international trial (83,905 operation records). The proposed system integrates three types of running environments and data engines in a single service-oriented architecture. This feature is used to assess key performance indicators comparing them with other type of architectures. Data sustainability across the applications has been evaluated showing better outcomes for full integrated sensors. At the same time, runtime performance of clients has been assessed spotting no differences regarding the operative environment.

  • @Article{Martinez-Millana2015, author = {Martinez-Millana, A. and Fico, G. and Fernandez-Llatas, C. and Traver,V.},
    title = {Performance assessment of a closed-loop system for diabetes management},
    journal = {Medical \& Biological Engineering \& Computing},
    year = {2015},
    volume = {2015},
    pages = {1--9},
    month = feb, issn = {0140-0118, 1741-0444},
    abstract = {Telemedicine systems can play an important role in the management of diabetes, a chronic condition that is increasing worldwide. Evaluations on the consistency of information across these systems and on their performance in a real situation are still missing. This paper presents a remote monitoring system for diabetes management based on physiological sensors, mobile technologies and patient/doctor applications over a service-oriented architecture that has been evaluated in an international trial (83,905 operation records). The proposed system integrates three types of running environments and data engines in a single service-oriented architecture. This feature is used to assess key performance indicators comparing them with other type of architectures. Data sustainability across the applications has been evaluated showing better outcomes for full integrated sensors. At the same time, runtime performance of clients has been assessed spotting no differences regarding the operative environment.},
    doi = {10.1007/s11517-015-1245-3},
    keywords = {Biomedical Engineering, Computer Applications, Diabetes, Human Physiology, Imaging / Radiology, KPI, mHealth, Performance, Sensors, SOA, Telemonitoring},
    language = {en},
    owner = {carferll},
    timestamp = {2015.02.17},
    url = {http://link.springer.com/article/10.1007/s11517-015-1245-3},
    urldate = {2015-02-17},
    }