The Evidence-base for Using Ontologies and Semantic Integration Methodologies to Support Integrated Chronic Disease Management in Primary and Ambulatory Care: Realist Review

Summary

Background: Most chronic diseases are managed in primary and ambulatory care. The chronic care model (CCM) suggests a wide range of community, technological, team and patient factors contribute to effective chronic disease management. Ontologies have the capability to enable formalised linkage of heterogeneous data sources as might be found across the elements of the CCM.

Objective: To describe the evidence base for using ontologies and other semantic integration methods to support chronic disease management.

Method: We reviewed the evidence-base for the use of ontologies and other semantic integration methods within and across the elements of the CCM. We report them using a realist review describing the context in which the mechanism was applied, and any outcome measures.

Results: Most evidence was descriptive with an almost complete absence of empirical research and important gaps in the evidence-base. We found some use of ontologies and semantic integration methods for community support of the medical home and for care in the community. Ubiquitous information technology (IT) and other IT tools were deployed to support self-management support, use of shared registries, health behavioural models and knowledge discovery tools to improve delivery system design. Data quality issues restricted the use of clinical data; however there was an increased use of interoperable data and health system integration.

Conclusions: Ontologies and semantic integration methods are emergent with limited evidence-base for their implementation. However, they have the potential to integrate the disparate community wide data sources to provide the information necessary for effective chronic disease management.

Keywords

Ontology - general practice - chronic disease - semantic web - knowledge management

 

• References

• 1 WHO Preventing chronic diseases: a vital investment. World Health Organization; Geneva: 2005
• 2 De Maeseneer J, Roberts RG, Demarzo M, Heath I, Sewankambo N, Kidd MR. et al. Tackling NCDs: a different approach is needed. Lancet 2012; May 19 379 (9829) 1860-1.
• 3 Lalonde L, Goudreau J, Hudon E, Lussier MT, Duhamel F, Bélanger D. et al. Group for TRANSIT to Best Practices in Cardiovascular Disease Prevention in Primary Care. Priorities for action to improve cardiovascular preventive care of patients with multimorbid conditions in primary care-a participatory action research project. Fam Pract 2012; Dec 29 (6) 733-41.
• 4 Bodenheimer T, Wagner EH, Grumbach K. Improving primary care for patients with chronic illness. JAMA 2002; 288 (14) 1775-9.
• 5 Bodenheimer T, Wagner EH, Grumbach K. Improving primary care for patients with chronic illness: the chronic care model, Part 2. JAMA 2002; 288 (15) 1909-14.
• 6 Cramm JM, Nieboer AP. In the Netherlands, rich interaction among professionals conducting disease management led to better chronic care. Health Aff (Millwood) 2012; 31 (11) 2493-500.
• 7 Siminerio LM. The role of technology and the chronic care model. J Diabetes Sci Technol 2010; 4 (2) 470-5.
• 8 Roblin DW. The potential of cellular technology to mediate social networks for support of chronic disease self-management. J Health Commun 2011; 16 Suppl 1 59-76.
• 9 Bott OJ, Ammenwerth E, Brigl B, Knaup P, Lang E, Pilgram R. et al. The challenge of ubiquitous computing in health care: technology, concepts and solutions. Methods Inf Med 2005; 44: 473-9.
• 10 Campbell MK, Tessaro I, Gellin M, Valle CG, Golden S, Kaye L. et al. Adult cancer survivorship care: experiences from the LIVESTRONG centers of excellence network. J Cancer Surviv 2011; Sep 5 (3) 271-82.
• 11 MacLean CD, Littenberg B, Gagnon M, Reardon M, Turner PD, Jordan C. The Vermont Diabetes Information System (VDIS): study design and subject recruitment for a cluster randomized trial of a decision support system in a regional sample of primary care practices. Clin Trials 2004; 1 (6) 532-44.
• 12 Mendis S, Al Bashir I, Dissanayake L, Varghese C, Fadhil I, Marhe E. et al. Gaps in capacity in primary care in low-resource settings for implementation of essential noncommunicable disease interventions. Int J Hypertens 2012; 2012: 584041.
• 13 Hwang KH, Chung KI, Chung MA, Choi D. Review of semantically interoperable electronic health records for ubiquitous healthcare. Healthc Inform Res 2010; 16 (1) 1-5.
• 14 National Research Council (US) Board on Research Data and Information. Uhlir PF. editor. Designing the Microbial Research Commons: Proceedings of an International Symposium. Washington (DC): National Academies Press (US); 2011
• 15 Cimino JJ. Collect once, use many. Enabling the reuse of clinical data through controlled terminologies. J AHIMA 2007; Feb 78 (2) 24-9.
• 16 Garde S, Chen R, Leslie H, Beale T, McNicoll I, Heard S. Archetype-based knowledge management for semantic interoperability of electronic health records. Stud Health Technol Inform 2009; 150: 1007-11.
• 17 Dolin RH, Alschuler L, Boyer S, Beebe C, Behlen FM, Biron PV. et al. HL, 7 Clinical Document Architecture, Release 2. J Am Med Inform Assoc 2006; 13 (1) 30-9.
• 18 Liaw ST, Rahimi A, Ray P, Taggart J, Dennis S, de Lusignan S. et al. Towards an ontology for data quality in integrated chronic disease management: A realist review of the literature. Int J Med Inform 2013; 82 (1) 10-24.
• 19 Gruber T. A translation approach to portable ontologies. Knowledge Acquisition 1993; 5 (2) 199-2.
• 20 Groen P, Wine M. Medical Semantics, Ontologies, Open Solutions and EHR Systems Virtual Medical Worlds. 2009 URL: http://www.hoise.com/vmw/09/articles/vmw/LV-VM-09-09-6.html
• 21 Pawson R, Tilley N. Realistic evaluation. London: Sage; 1997
• 22 de Lusignan S, Pritchard K, Chan T. A knowledge-management model for clinical practice. J Postgrad Med 2002; 0ct-Dec 48 (4) 297-303.
• 23 Donabedian A. Evaluating the quality of medical care. Milbank Q 2005; 83 (4) 691-729.
• 24 Cochrane Effective Practice and Organisation of Care (EPOC) Group taxonomy of outcomes. URL: http://epoc.cochrane.org/
• 25 Atkins D, Best D, Briss PA, Eccles M, Falck-Ytter Y, Flottorp S. et al GRADE Working Group. Grading quality of evidence and strength of recommendations. BMJ 2004; 328 (7454) 1490.
• 26 Plumb J, Weinstein LC, Brawer R, Scott K. Community-based partnerships for improving chronic disease management. Prim Care 2012; Jun 39 (2) 433-47.
• 27 Paganelli F, Giuli D. An ontology-based system for context-aware and configurable services to support home-based continuous care. IEEE Trans Inf Technol Biomed 2011; 15 (2) 324-33.
• 28 Rosso R, Munaro G, Salvetti O, Colantonio S, Ciancitto F. CHRONIOUS: an open, ubiquitous and adaptive chronic disease management platform for chronic obstructive pulmonary disease (COPD), chronic kidney disease (CKD) and renal insufficiency. Conf Proc IEEE Eng Med Biol Soc 2010; 2010: 6850-3.
• 29 Enderlin CA, McLeskey N, Rooker JL, Steinhauser C, D'Avolio D, Gusewelle R. et al. Review of current conceptual models and frameworks to guide transitions of care in older adults. Geriatr Nurs. 2012 Oct 31 doi:pii:S0197-4572(12)00278-9 10.1016/j.gerinurse.2012.08.003.
• 30 van der Linden BA, Spreeuwenberg C, Schrijvers AJ. Integration of care in The Netherlands: the development oftransmural care since 1994. Health Policy 2001; 55 (2) 111-20.
• 31 Martinez-Lopez R, Millan-Ruiz D, Martin-Dominguez A, Toro-Escudero M. An Architecture for Next-Generation of Telecare Systems Using Ontologies, Rules Engines and Data Mining, Computational Intelligence for Modelling Control & Automation, 2008 International Conference. 2008: 31-6.
• 32 NHS Data model and Dictionary Service. URL: http://www.connectingforhealth.nhs.uk/system-sandservices/data/nhsdmds
• 33 Gunther S, Taub N, Rogers S, Baker R. What aspects of primary care predict emergency admission rates? A cross sectional study. BMC Health Serv Res. 2013 Jan 7 13. 11.
• 34 Agboado G, Peters J, Donkin L. Factors influencing the length ofhospital stay among patients resident in Blackpool admitted with COPD: a cross-sectional study. BMJ Open. 2012 Sep 1 2. (5).
• 35 Berwick DM. Developing and testing changes in delivery of care. Ann Intern Med 1998; 128: 651-6.
• 36 Kilo CM. A framework for collaborative improvement: lessons from the Institute for Healthcare Improvement's Breakthrough Series. Qual Manag Health Care 1998; 6: 1-13.
• 37 Baer D. Patient-physician e-mail communication: the kaiser permanente experience. J Oncol Pract 2011; 7 (4) 230-3.
• 38 de Lusignan S, Seroussi B. A comparison of English and French approaches to providing patients access to summary care records: scope, consent, cost. Accepted for publication EFMI STC 2013. Prague; (Ref: 51).
• 39 Kokkinaki A, Chouvarda I, Maglaveras N. An ontology-based approach facilitating unified querying of biosignals and patient records. Conf Proc IEEE Eng Med Biol Soc 2008; 2008: 2861-4.
• 40 de Lusignan S, Mimnagh C. Breaking the first law of informatics: the Quality and Outcomes Framework (QOF) in the dock. Inform Prim Care 2006; 14 (3) 153-6.
• 41 Stevens PE, de Lusignan S, Farmer CK, Tomson CR. Engaging primary care in CKD initiatives: the UK experience. Nephrol Dial Transplant 2012; 27 Suppl 3 iii5-iii11.
• 42 de Lusignan S. Informatics as tool for quality improvement: rapid implementation of guidance for the management of chronic kidney disease in England as an exemplar. Healthc Inform Res 2013; Mar 19 (1) 9-15.
• 43 Dhoul N, de Lusignan S, Dmitrieva O, Stevens P, O'Donoghue D. Quality achievement and disease prevalence in primary care predicts regional variation in renal replacement therapy (RRT) incidence: an ecological study. Nephrol Dial Transplant 2012; 27 (2) 739-46.
• 44 Protti D, Bowden T, Johansen I. Adoption of information technology in primary care physician offices in New Zealand and Denmark, part 5: final comparisons. Inform Prim Care 2009; 17 (1) 17-22.
• 45 Tufano JT, Ralston JD, Tarczy-Hornoch P, Reid RJ. Participatory (re)design of a sociotechnical healthcare delivery system: the Group Health Patient-Centered Medical Home. Stud Health Technol Inform 2010; 157: 59-65.
• 46 Lee A, Siu CF, Leung KT, Lau LC, Chan CC. Wong KK. General practice and social service partnership for better clinical outcomes, patient self efficacy and lifestyle behaviours of diabetic care: randomised control trial of a chronic care model. Postgrad Med J 2011; Oct 87 (1032) 688-93.
• 47 Mabotuwana T, Warren J. An ontology-based approach to enhance querying capabilities of general practice medicine for better management of hypertension. Artif Intell Med 2009; 47 (2) 87-103.
• 48 Borycki E, Kushniruk A. Development ofa virtual self-management tool for COPD patients: towards a user needs ontology. AMIA Annu Symp Proc 2007; 879.
• 49 Eccher C, Purin B, Pisanelli D, Battaglia M, Apolloni I, Forti S. Ontologies supporting continuity of care: The case ofheart failure. Comput i Biol Med 2006; 36 (7) 789-801.
• 50 Harwood L, Clark AM. Understanding health decisions using critical realism: home-dialysis decision-making during chronic kidney disease. Nurs Inq 2012; 19 (1) 29-38.
• 51 Fullwood C, Kennedy A, Rogers A, Eden M, Gardner C, Protheroe J. et al. Patients' Experiences of Shared Decision Making in Primary Care Practices in the United Kingdom. Med Decis Making. 2012 Nov 5.
• 52 Dennis SM, Zwar N, Griffiths R, Roland M, Hasan I, Powell Davies G. et al. Chronic disease management in primary care: from evidence to policy. Med J Aust 2008; 188 (8 Suppl) s53-6.
• 53 Johnston S, Liddy C, Mill K, Irving H. Building the Evidence Base for Chronic Disease Self-management Support Interventions Across Canada. Can J Public Health 2012; 103 (6) e462-7.
• 54 Protheroe J, Rogers A, Kennedy AP, Macdonald W, Lee V. Promoting patient engagement with self-management support information: a qualitative meta-synthesis of processes influencing uptake. Implement Sci. 2008 Oct 13 3. 44.
• 55 Lenert L, Norman GJ, Mailhot M, Patrick K. A framework for modeling health behavior protocols and their linkage to behavioral theory. J Biomed Inform 2005; 38 (4) 270-80.
• 56 Vrijhoef H, Spreeuwenberg C, Eijkelberg I. et al. Adoption of disease management model for diabetes in region of Maastricht. BMJ 2001; 323: 983-5.
• 57 Ahgren B. Chain of Care development in Sweden: results of a national study. Int J Integr Care 2003; 3: e01.
• 58 Gravelle H, Dusheiko M, Sheaff R, Sargent P, Boaden R, Pickard S. et al. Impact of case management (Evercare) on frail elderly patients: controlled before and after analysis of quantitative outcome data. BMJ 2007; 334: 31.
• 59 Schmittdiel J, Mosen DM, Glasgow RE, Hibbard J, Remmers C, Bellows J. Patient Assessment of Chronic Illness Care (PACIC) and improved patient-centered outcomes for chronic conditions. J Gen Intern Med 2008; Jan 23 (1) 77-80.
• 60 Rick J, Rowe K, Hann M, Sibbald B, Reeves D, Roland M, Bower P. Psychometric properties of the patient assessment of chronic illness care measure: acceptability, reliability and validity in United Kingdom patients with long-term conditions. BMC Health Serv Res. 2012 Aug 31 12. 293.
• 61 Wagholikar KB, Sundararajan V, Deshpande AW. Modeling paradigms for medical diagnostic decision support: a survey and future directions. J Med Syst 2012; 36 (5) 3029-49.
• 62 Abidi S. A conceptual framework for ontology based automating and merging of clinical pathways of comorbidities. Knowledge Management for Health Care Procedures 2009; 55-66.
• 63 Verma A, Fiasché M, Cuzzola M, Iacopino P, Morabito F, Kasabov N. Ontology Based Personalized Modeling for Type 2 Diabetes Risk Analysis: An Integrated Approach. In: Neural Information Processing. Berlin/Heidelberg: Springer; 2009. p. 360-6.
• 64 Verma A, Fiasché M, Cuzzola M, Morabito F, Irrera G. Knowledge Discovery and Risk Prediction for Chronic Diseases: An Integrated Approach. Engineering Applications of Neural Networks 2011; 270-9.
• 65 Rodríguez-González A, Mayer MA, Alor-Hernandez G, Gomez-Berbis JM, Cortes-Robles G, Lemos AL. Using ontologies and probabilistic networks to develop a preventive stroke diagnosis system (PSDS). In: Computer-Based Medical Systems (CBMS). IEEE 23rd International Symposium. 2013: 370-7.
• 66 de Lusignan S, Liaw ST, Michalakidis G, Jones S. Defining datasets and creating data dictionaries for quality improvement and research in chronic disease using routinely collected data: an ontology-driven approach. Inform Prim Care 2011; 19 (3) 127-34.
• 67 Campbell S, Roland M, Middleton E, Reeves D. Improvements in quality of clinical care in English general practice 1998-2003: longitudinal observational study. BMJ 2005; 331: 1121.
• 68 Murray J, Saxena S, Millett C, Curcin V, de Lusignan S, Majeed A. Reductions in risk factors for secondary prevention of coronary heart disease by ethnic group in south-west London: 10-year longitudinal study (1998-2007). Fam Pract 2010; 27 (4) 430-8.
• 69 Lincoln MJ, Weir C, Moreshead G, Kolodner R, Williamson J. Creating and evaluating the Department of Veterans Affairs electronic medical record and national clinical lexicon. Proc Annu Symp Comput Appl Med Care. 1994: 1047.
• 70 Eagon JC, Hurdle JF, Lincoln MJ. Inter-rater reliability and review of the VA unresolved narratives. Proc AMIA Annu Fall Symp 1996; 130-4.
• 71 Penz JF, Brown SH, Carter JS, Elkin PL, Nguyen VN, Sims SA, Lincoln MJ. Evaluation of SNOMED coverage of Veterans Health Administration terms. Stud Health Technol Inform 2004; 107 (Pt 1) 540-4.
• 72 MacLean CD, Littenberg B, Gagnon M. Diabetes decision support: initial experience with the Vermont diabetes. Am J Public Health 2006; 96 (4) 593-5.
• 73 Natter MD, Quan J, Ortiz DM, Bousvaros A, Ilowite NT, Inman CJ. et al. An i2b2-based, generalizable, open source, self-scaling chronic disease registry. J Am Med Inform Assoc 2013; Jan 1 20 (1) 172-9.
• 74 Benedetti R, Flock B, Pedersen S, Ahern M. Improved clinical outcomes for fee-for-service physician practices participating in a diabetes care collaborative. Jt Comm J Qual Saf 2004; 30 (4) 187-94.
• 75 Rossi Mori A, Mazzeo M, Mercurio G, Verbicaro R. Holistic health: Predicting our data future (from inter-operability among systems to co-operability among people). Int J Med Inform. 2012 Oct 31.
• 76 de Lusignan S, Wells SE, Hague NJ, Thiru K. Managers see the problems associated with coding clinical data as a technical issue whilst clinicians also see cultural barriers. Methods Inf Med 2003; 42 (4) 416-22.
• 77 Soler JK, Okkes I, Wood M, Lamberts H. The coming of age of ICPC: celebrating the 21st birthday of the International Classification of Primary Care. Fam Pract 2008; 25: 312-7.
• 78 Soler JK, Okkes I. Reasons for Encounter and symptom diagnoses: a superior description of patients' problems in contrast to medically unexplained symptoms (MUS). Fam Pract 2012; 29: 272-82.
• 79 Cramm JM, Nieboer AP. Relational coordination promotes quality of chronic care delivery in Dutch disease-management programs. Health Care Manage Rev 2012; 0ct-Dec 37 (4) 301-9.
• 80 Styles C, Timlin H. The relevance ofblood pressure measurements in patients attending a diabetic eye clinic: the use of an electronic patient record. Eye (Lond) 2007; Nov 21 (11) 1450.
• 81 Brem H, Sheehan P, Rosenberg HJ, Schneider JS, Boulton AJ. Evidence-based protocol for diabetic foot ulcers. Plast Reconstr Surg 2006; Jun 117 (7 Suppl) 193S-209S discussion 210S-211S.
• 82 Bioinformatics: towards new directions for public health. Methods Inf Med 2004; 43 (3) 208-14.
• 83 de Lusignan S, Krause P, Michalakidis G, Vicente MT, Thompson S, McGilchrist M. et al. Business Process Modelling is an Essential Part of a Requirements Analysis. Contribution of EFMI Primary Care Working Group. Yearb Med Inform 2012; 7 (1) 34-43.
• 84 Kawazoe Y, Ohe K. An ontology-based mediator of clinical information for decision support systems: a prototype of a clinical alert system for prescription. Methods Inf Med 2008; 47 (6) 549-59.
• 85 Corrigan D, Taweel A, Fahey T, Arvanitis T, Delaney B. An ontolological treatment of clinical prediction rules implementing the alvarado score. Stud Health Technol Inform 2013; 186: 103-7.