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.

[1]  Pablo Alonso-Coello,et al.  Guidance for updating clinical practice guidelines: a systematic review of methodological handbooks , 2014, Implementation Science.

[2]  Frank van Harmelen,et al.  Inferring recommendation interactions in clinical guidelines , 2016, Semantic Web.

[3]  Wil M.P. van der Aalst,et al.  Process mining with the HeuristicsMiner algorithm , 2006 .

[4]  Jonathan M. Teich,et al.  Grand challenges in clinical decision support , 2008, J. Biomed. Informatics.

[5]  Jiangbo Dang,et al.  An ontological knowledge framework for adaptive medical workflow , 2008, J. Biomed. Informatics.

[6]  Frank van Harmelen,et al.  Towards a Conceptual Model for Enhancing Reasoning About Clinical Guidelines - A Case-Study on Comorbidity , 2014, KR4HC@VSL.

[7]  D. Moher,et al.  Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. , 2010, International journal of surgery.

[8]  Gert P. Westert,et al.  Current Guidelines Have Limited Applicability to Patients with Comorbid Conditions: A Systematic Analysis of Evidence-Based Guidelines , 2011, PloS one.

[9]  M. Tonelli,et al.  The challenge of evidence in clinical medicine. , 2010, Journal of evaluation in clinical practice.

[10]  Wil M. P. van der Aalst,et al.  Process Mining Techniques: an Application to Stroke Care , 2008, MIE.

[11]  Jeffrey K Aronson,et al.  Medication errors: definitions and classification. , 2009, British journal of clinical pharmacology.

[12]  Sonja J Lewis,et al.  The importance and impact of evidence-based medicine. , 2004, Journal of managed care pharmacy : JMCP.

[13]  Joel French,et al.  Applying Science to Medicine , 2010 .

[14]  Samina Raza Abidi,et al.  Using OWL Ontologies for Clinical Guidelines Based Comorbid Decision Support , 2012, 2012 45th Hawaii International Conference on System Sciences.

[15]  Xing Tan,et al.  Towards a formal representation of clinical practice guidelines for the treatment of comorbid patients , 2013, 2013 IEEE International Conference on Bioinformatics and Biomedicine.

[16]  Jacques Bouaud,et al.  Clinical Practice Guidelines Consistency for Patients with Multimorbidity: a Case-Study in the Management of Type 2 Diabetes and Hypertension , 2015, MIE.

[17]  S. Abidi.  A KNOWLEDGE MANAGEMENT FRAMEWORK TO DEVELOP, MODEL, ALIGN AND OPERATIONALIZE CLINICAL PATHWAYS TO PROVIDE DECISION SUPPORT FOR COMORBID DISEASES , 2010 .

[18]  S. Wyke,et al.  Epidemiology of multimorbidity and implications for health care, research, and medical education: a cross-sectional study , 2012, The Lancet.

[19]  Huilong Duan,et al.  Summarizing clinical pathways from event logs , 2013, J. Biomed. Informatics.

[20]  Eva Onaindia,et al.  A Multi-agent Planning Approach for the Generation of Personalized Treatment Plans of Comorbid Patients , 2013, AIME.

[21]  Xing Tan,et al.  An ontological and hierarchical approach for supply chain event aggregation , 2015, Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015).

[22]  Wojtek Michalowski,et al.  A Constraint Logic Programming Approach to Identifying Inconsistencies in Clinical Practice Guidelines for Patients with Comorbidity , 2011, AIME.

[23]  David Riaño,et al.  Automatic generation of clinical algorithms within the state-decision-action model , 2012, Expert Syst. Appl..

[24]  Huilong Duan,et al.  On mining clinical pathway patterns from medical behaviors , 2012, Artif. Intell. Medicine.

[25]  Ramakrishnan Srikant,et al.  Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.

[26]  Max Welling,et al.  Distributed Algorithms for Topic Models , 2009, J. Mach. Learn. Res..

[27]  Frank van Harmelen,et al.  A Conceptual Model for Detecting Interactions among Medical Recommendations in Clinical Guidelines - A Case-Study on Multimorbidity , 2014, EKAW.

[28]  Samina Raza Abidi,et al.  Operationalizing Prostate Cancer Clinical Pathways: An Ontological Model to Computerize, Merge and Execute Institution-Specific Clinical Pathways , 2009, K4HelP.

[29]  M. Dawes.  Co-morbidity: we need a guideline for each patient not a guideline for each disease. , 2010, Family practice.

[30]  David Riaño,et al.  An ontology-based personalization of health-care knowledge to support clinical decisions for chronically ill patients , 2012, J. Biomed. Informatics.

[31]  Samina Raza Abidi.  Ontology-Based Knowledge Modeling to Provide Decision Support for Comorbid Diseases , 2010, KR4HC.

[32]  J. Grimshaw,et al.  Potential benefits, limitations, and harms of clinical guidelines , 1999, BMJ.

[33]  Jacques Bouaud,et al.  Synthesis of Elementary Single-Disease Recommendations to Support Guideline-Based Therapeutic Decision for Complex Polypathological Patients , 2004, MedInfo.

[34]  Yang Chen,et al.  Network Analysis of Human Disease Comorbidity Patterns Based on Large-Scale Data Mining , 2014, ISBRA.

[35]  Samina Raza Abidi,et al.  Towards the Merging of Multiple Clinical Protocols and Guidelines via Ontology-Driven Modeling , 2009, AIME.

[36]  W. Michalowski,et al.  Identifying inconsistencies in multiple clinical practice guidelines for a patient with co-morbidity , 2010, 2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW).

[37]  D. A. Bottino,et al.  Decision Support System on Hypertension Treatment , 2009 .

[38]  A. Helvik,et al.  Applying clinical guidelines in general practice: a qualitative study of potential complications , 2016, BMC Family Practice.

[39]  Wojtek Michalowski,et al.  Using Constraint Logic Programming to Implement Iterative Actions and Numerical Measures during Mitigation of Concurrently Applied Clinical Practice Guidelines , 2013, AIME.

[40]  T. Rosemann,et al.  Co- and multimorbidity patterns in primary care based on episodes of care: results from the German CONTENT project , 2008, BMC health services research.

[41]  Bohada Jaime,et al.  Automatic production and integration of knowledge to the support of the decision and planning activities in medical-clinical diagnosis, treatment and prognosis. , 2012 .

[42]  David Riaño,et al.  Model-Based Combination of Treatments for the Management of Chronic Comorbid Patients , 2013, AIME.

[43]  David Riaño,et al.  An Autonomous Algorithm for Generating and Merging Clinical Algorithms , 2008, K4HelP.

[44]  B. Starfield,et al.  Defining Comorbidity: Implications for Understanding Health and Health Services , 2009, The Annals of Family Medicine.

[45]  Szymon Wilk,et al.  Using First-Order Logic to Represent Clinical Practice Guidelines and to Mitigate Adverse Interactions , 2014, KR4HC@VSL.

[46]  Manfred Reichert,et al.  IT support for healthcare processes - premises, challenges, perspectives , 2007, Data Knowl. Eng..

[47]  jafarpour borna,et al.  ONTOLOGY MERGING USING SEMANTICALLY-DEFINED MERGE CRITERIA AND OWL REASONING SERVICES: TOWARDS EXECUTION-TIME MERGING OF MULTIPLE CLINICAL WORKFLOWS TO HANDLE COMORBIDITIES , 2014 .

[48]  Samina Raza Abidi.  A Conceptual Framework for Ontology Based Automating and Merging of Clinical Pathways of Comorbidities , 2008, K4HelP.

[49]  Wil M. P. van der Aalst,et al.  Process Mining in Healthcare , 2020, Interactive Process Mining in Healthcare.

[50]  S. Dovey,et al.  Classification of medical errors and preventable adverse events in primary care: a synthesis of the literature. , 2002, The Journal of family practice.

[51]  Alberto Moreno-Conde,et al.  Sharing clinical decisions for multimorbidity case management using social network and open-source tools , 2013, J. Biomed. Informatics.

[52]  Nguyen Phuoc Long,et al.  Research Trends in Evidence-Based Medicine: A Joinpoint Regression Analysis of More than 50 Years of Publication Data , 2015, PloS one.

[53]  Cheryl L. Damberg,et al.  Episode-based performance measurement and payment: making it a reality. , 2009, Health affairs.

[54]  Walter Daelemans,et al.  Automatic discovery of workflow models from hospital data , 2001 .

[55]  Syed Sibte Raza Abidi,et al.  Merging Disease-Specific Clinical Guidelines to Handle Comorbidities in a Clinical Decision Support Setting , 2013, AIME.

[56]  L. Fratiglioni,et al.  Aging with multimorbidity: A systematic review of the literature , 2011, Ageing Research Reviews.

[57]  David Riaño,et al.  Automatic Combination of Formal Intervention Plans Using SDA* Representation Model , 2007, K4CARE.

[58]  Huilong Duan,et al.  Latent Treatment Pattern Discovery for Clinical Processes , 2013, Journal of Medical Systems.

[59]  Chris Salisbury,et al.  Prevalence, Determinants and Patterns of Multimorbidity in Primary Care: A Systematic Review of Observational Studies , 2014, PloS one.

[60]  David Riaño,et al.  An Ontology for the Care of the Elder at Home , 2009, AIME.

[61]  Ravi Varadhan,et al.  Multimorbidity and Evidence Generation , 2014, Journal of General Internal Medicine.

[62]  J. Ainsworth,et al.  Adoption of Clinical Decision Support in Multimorbidity: A Systematic Review , 2015, JMIR medical informatics.

[63]  A R Feinstein,et al.  THE PRE-THERAPEUTIC CLASSIFICATION OF CO-MORBIDITY IN CHRONIC DISEASE. , 1970, Journal of chronic diseases.

[64]  David Riaño,et al.  The SDA Model: A Set Theory Approach , 2007, Twentieth IEEE International Symposium on Computer-Based Medical Systems (CBMS'07).

[65]  Martin Fortin,et al.  Future of Multimorbidity Research: How Should Understanding of Multimorbidity Inform Health System Design? , 2010 .

[66]  S. Riedel-Heller,et al.  Review: Health Care Utilization and Costs of Elderly Persons With Multiple Chronic Conditions , 2011, Medical care research and review : MCRR.

[67]  Wil M. P. van der Aalst,et al.  Process Mining: Overview and Opportunities , 2012, ACM Trans. Manag. Inf. Syst..

[68]  B. Guthrie,et al.  Guidelines for people not for diseases: the challenges of applying UK clinical guidelines to people with multimorbidity. , 2013, Age and ageing.

[69]  Wojtek Michalowski,et al.  First-Order Logic Theory for Manipulating Clinical Practice Guidelines Applied to Comorbid Patients: A Case Study , 2014, AMIA.

[70]  Huilong Duan,et al.  Discovery of clinical pathway patterns from event logs using probabilistic topic models , 2014, J. Biomed. Informatics.

[71]  W. Michalowski,et al.  Reconciliation of concurrently applied clinical practice guidelines using Constraint Logic Programming , 2011, Proceedings of the 6th International Symposium on Health Informatics and Bioinformatics.

[72]  Diogo R. Ferreira,et al.  Business process analysis in healthcare environments: A methodology based on process mining , 2012, Inf. Syst..

[73]  Wojtek Michalowski,et al.  Mitigation of adverse interactions in pairs of clinical practice guidelines using constraint logic programming , 2013, J. Biomed. Informatics.

[74]  Huilong Duan,et al.  On mining latent treatment patterns from electronic medical records , 2015, Data Mining and Knowledge Discovery.

[75]  Samson W. Tu,et al.  Mining Hospital Data to Learn SDA* Clinical Algorithms , 2007, K4CARE.

[76]  Mor Peleg,et al.  Computer-interpretable clinical guidelines: A methodological review , 2013, J. Biomed. Informatics.

[77]  Zhizheng Zhang,et al.  Preliminary Result on Finding Treatments for Patients with Comorbidity , 2014, KR4HC@VSL.

[78]  Joan Albert,et al.  Knowledge-based incremental induction of clinical algorithms , 2012 .

[79]  David Riaño,et al.  Rule-Based Combination of Comorbid Treatments for Chronic Diseases Applied to Hypertension, Diabetes Mellitus and Heart Failure , 2012, ProHealth/KR4HC.

[80]  Oliver Reich,et al.  Multimorbidity, health care utilization and costs in an elderly community-dwelling population: a claims data based observational study , 2015, BMC Health Services Research.

[81]  Juan Fernández-Olivares,et al.  A Common-Recipe and Conflict-Solving MAP Approach for Care Planning in Comorbid Patients , 2013, CAEPIA.

[82]  Aziz A. Boxwala,et al.  Ontological approach for safe and effective polypharmacy prescription , 2012, AMIA.

[83]  M. Jakovljevič,et al.  Comorbidity and multimorbidity in medicine today: challenges and opportunities for bringing separated branches of medicine closer to each other. , 2013, Psychiatria Danubina.

[84]  Wil M. P. van der Aalst,et al.  Application of Process Mining in Healthcare - A Case Study in a Dutch Hospital , 2008, BIOSTEC.

[85]  Chris Salisbury,et al.  Multimorbidity: redesigning health care for people who use it , 2012, The Lancet.

[86]  Arie Hasman,et al.  Approaches for creating computer-interpretable guidelines that facilitate decision support , 2004, Artif. Intell. Medicine.

[87]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[88]  Mar Marcos,et al.  Experiences in the Development of Electronic Care Plans for the Management of Comorbidities , 2009, KR4HC.

[89]  Mark Spranca,et al.  Reduction in medication errors in hospitals due to adoption of computerized provider order entry systems , 2013, J. Am. Medical Informatics Assoc..