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Methods Inf Med 2015; 54(06): 479-487
DOI: 10.3414/ME15-01-0064
Original Articles
Schattauer GmbH

Combining Health Data Uses to Ignite Health System Learning

J. Ainsworth
1   Health eResearch Centre, Farr Institute for Health Informatics Research, University of Manchester, Manchester, UK
2   Centre for Health Informatics, Institute of Population Health, University of Manchester, Manchester, UK
,
I. Buchan
1   Health eResearch Centre, Farr Institute for Health Informatics Research, University of Manchester, Manchester, UK
2   Centre for Health Informatics, Institute of Population Health, University of Manchester, Manchester, UK
› Institutsangaben
Weitere Informationen

Publikationsverlauf

received: 05. Mai 2015

accepted: 09. Juni 2015

Publikationsdatum:
23. Januar 2018 (online)

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Summary

Objectives: In this paper we aim to characterise the critical mass of linked data, methods and expertise required for health systems to adapt to the needs of the populations they serve – more recently known as learning health systems. The objectives are to: 1) identify opportunities to combine separate uses of common data sources in order to reduce duplication of data processing and improve information quality; 2) identify challenges in scaling-up the reuse of health data sufficiently to support health system learning.

Methods: The challenges and opportunities were identified through a series of e-health stakeholder consultations and workshops in Northern England from 2011 to 2014. From 2013 the concepts presented here have been refined through feedback to collaborators, including patient/citizen representatives, in a regional health informatics research network (www.herc.ac.uk).

Results: Health systems typically have separate information pipelines for: 1) commissioning services; 2) auditing service performance; 3) managing finances; 4) monitoring public health; and 5) research. These pipelines share common data sources but usually duplicate data extraction, aggregation, cleaning/preparation and analytics. Suboptimal analyses may be performed due to a lack of expertise, which may exist elsewhere in the health system but is fully committed to a different pipeline. Contextual knowledge that is essential for proper data analysis and interpretation may be needed in one pipeline but accessible only in another. The lack of capable health and care intelligence systems for populations can be attributed to a legacy of three flawed assumptions: 1) universality: the generalizability of evidence across populations; 2) time-invariance: the stability of evidence over time; and 3) reducibility: the reduction of evidence into specialised subsystems that may be recombined.

Conclusions: We conceptualize a population health and care intelligence system capable of supporting health system learning and we put forward a set of maturity tests of progress toward such a system. A factor common to each test is data-action latency; a mature system spawns timely actions proportionate to the information that can be derived from the data, and in doing so creates meaningful measurement about system learning. We illustrate, using future scenarios, some major opportunities to improve health systems by exchanging conventional intelligence pipelines for networked critical masses of data, methods and expertise that minimise data-action latency and ignite system-learning.

Keywords

Health data reuse - secondary uses - meaningful use - learning health systems - adaptive health systems - intelligence pipelines - health-care evidence - population health
 

  • References

  • 1 Lafond S, Charlesworth A, Roberts A.. Hospital finances and productivity: in a critical condition?. London: 2015
    Google Scholar
  • 2 Neville S.. Financial crisis adds to health providers’ woes - FT.com. Financ Times 2015
    Google Scholar
  • 3 Fineberg H. V. A Successful and Sustainable Health System - How to Get There from Here. N Engl. J Med 2012; 366 (11) 1020-1027
    PubMedGoogle Scholar
  • 4 Wanless D, Appleby J, Harrison A, Patel D.. Our Future Health Secured?: A review of NHS funding and performance. London: 2007
    Google Scholar
  • 5 Rigby M. Personal Health, Person-centred Health and Personalised Medicine - Concepts, Consumers, Confusion and Challenges in the Informatics World. Yearb Med Inform 2012; 7 (01) 7-15
    Artikel in Thieme ConnectPubMedGoogle Scholar
  • 6 Haycox A, Pirmohamed M, McLeod C, Houten R, Richards S. Through a glass darkly: economics and personalised medicine. Pharmacoeconomics 2014; 32 (11) 1055-1061
    CrossrefPubMedGoogle Scholar
  • 7 The Stationary Office. Health and Social Care Act 2012. UK: 2012
  • 8 Friedman C, Rubin J, Brown J, Buntin M, Corn M, Etheredge L, Gunter C, Musen M, Platt R, Stead W, Sullivan K, Van Houweling D.. Toward a science of learning systems: a research agenda for the high-functioning Learning Health System. J Am Med Informatics Assoc 2014; 22 (01) 43-50
    PubMedGoogle Scholar
  • 9 Friedman CP, Wong AK, Blumenthal D. Achieving a nationwide learning health system. Sci Transl Med 2010; 2: 57cm29
    CrossrefPubMedGoogle Scholar
  • 10 Ainsworth JD, Buchan IE. e-Labs and Work Objects: Towards Digital Health Economies. In: Mehmood R, Cerqueira E, Piesiewicz R, Chlamtac I. editors. Berlin, Heidelberg: Springer Berlin Heidelberg; 2009: 205-216
    Google Scholar
  • 11 Powell J, Buchan I. Electronic health records should support clinical research. J Med Internet Res 2005; 7 (01) e4
    CrossrefPubMedGoogle Scholar
  • 12 Valderas JM, Starfield B, Sibbald B, Salisbury C, Roland M. Defining comorbidity: implications for understanding health and health services. Ann Fam Med 2009; 7 (04) 357-363
    CrossrefPubMedGoogle Scholar
  • 13 Fortin M, Dionne J, Pinho G, Gignac J, Almirall J, Lapointe L. Randomized Controlled Trials: Do They Have External Validity for Patients with Multiple Comorbidities?. Ann Fam Med 2006; 4: 104-108
    CrossrefPubMedGoogle Scholar
  • 14 Geissbuhler A, Safran C, Buchan I, Bellazzi R, Labkoff S, Eilenberg K, Leese A, Richardson C, Mantas J, Murray P, De Moor G. Trustworthy reuse of health data: A transnational perspective. Int. J Med Inform 2013; 82: 1-9
    PubMedGoogle Scholar
  • 15 Bechhofer S, Buchan I, De Roure D, Missier P, Ainsworth J, Bhagat J, Couch P, Cruickshank D, Delderfield M, Dunlop I, Gamble M, Michaelides D, Owen S, Newman D, Sufi S, Goble C.. Why linked data is not enough for scientists. In: Future Generation Computer Systems 2013: 599-611
    Google Scholar
  • 16 Abernethy AP, Wheeler JL. True translational research: bridging the three phases of translation through data and behavior. Transl Behav Med 2011; 1 (01) 26-30
    CrossrefPubMedGoogle Scholar
  • 17 Grant SW, Hickey GL, Dimarakis I, Cooper G, Jenkins DP, Uppal R, Buchan I, Bridgewater B. Performance of the EuroSCORE models in emergency cardiac surgery. Circ Cardiovasc Qual Outcomes 2013; 6 (02) 178-185
    CrossrefPubMedGoogle Scholar
  • 18 Hickey GL, Grant SW, Murphy GJ, Bhabra M, Pagano D, McAllister K, Buchan I, Bridgewater B. Dynamic trends in cardiac surgery: why the logistic EuroSCORE is no longer suitable for contemporary cardiac surgery and implications for future risk models. Eur J Cardiothorac Surg 2013; 43 (06) 1146-1152
    CrossrefPubMedGoogle Scholar
  • 19 Nesta. People Powered Health: Co-Production Catalogue 2012
  • 20 Lötvall J, Akdis CA, Bacharier LB, Bjermer L, Casale TB, Custovic A, Lemanske RF, Wardlaw AJ, Wenzel SE, Greenberger PA. Asthma endotypes: A new approach to classification of disease entities within the asthma syndrome. J Allergy Clin Immunol 2011; 127: 355-360
    CrossrefPubMedGoogle Scholar
  • 21 New J, Aung T, Baker P, Yongsheng G, Pylypczuk R, Houghton J, Rudenski A, New R, Hegarty J, Gibson J, O’Donoghue D, Buchan I.. The high prevalence of unrecognized anaemia in patients with diabetes and chronic kidney disease: a population-based study. Diabet Med 2008; 25 (05) 564-569
    CrossrefPubMedGoogle Scholar
  • 22 Lependu P, Iyer S V, Fairon C, Shah NH. Annotation Analysis for Testing Drug Safety Signals using Unstructured Clinical Notes. J Biomed Semantics 2012; 3 Suppl (Suppl. 01) S5
    PubMedGoogle Scholar
  • 23 Overhage JM, Ryan PB, Schuemie MJ, Stang PE. Desideratum for evidence based epidemiology. Drug Saf 2013; 36 Suppl (Suppl. 01) S5-14
    CrossrefPubMedGoogle Scholar
  • 24 Information Governance Taskforce Privacy Impact Assessment Risk Stratification. Leeds: 2014
    Google Scholar
  • 25 Khokhar RH, Chen R, Fung BCM, Lui SM. Quantifying the costs and benefits of privacy-preserving health data publishing. J Biomed Inform 2014; 50: 107-121
    CrossrefPubMedGoogle Scholar
  • 26 Kuhn KA, Knoll A, Mewes H-W, Schwaiger M, Bode A, Broy M, Daniel H, Feussner H, Gradinger R, Hauner H, Höfler H, Holzmann B, Horsch A, Kemper A, Krcmar H, Kochs EF, Lange R, Leidl R, Mansmann U, Mayr EW, Meitinger T, Molls M, Navab N, Nüsslin F, Peschel C, Reiser M, Ring J, Rummeny EJ, Schlichter J, Schmid R, Wichmann HE, Ziegler S. Informatics and medicine - from molecules to populations. Methods Inf Med 2008; 47 (04) 283-295
    Artikel in Thieme ConnectPubMedGoogle Scholar
  • 27 James Manyika, Michael Chui, Brad Brown, Jacques Bughin, Richard Dobbs, Charles Roxburgh AHB Big data: The next frontier for innovation, competition, and productivity. . McKinsey Glob Inst. 2011. (May) 156
    Google Scholar
  • 28 BBSRC AND MRC Review of Vulnerable Skills and Capabilities London: 2014
  • 29 Thompson S, Kaptoge S, White I, Wood A, Perry P, Danesh J. Statistical methods for the time-to-event analysis of individual participant data from multiple epidemiological studies. Int J Epidemiol 2010; 39 (05) 1345-1359
    CrossrefPubMedGoogle Scholar
  • 30 Ioannidis JPA. Why most published research findings are false. PLoS Med 2005; 2 (08) e124
    CrossrefPubMedGoogle Scholar
  • 31 Pélissié Du Rausas M, Manyika J, Hazan E, Bughin J, Chui M, Said R.. Internet matters: The Net’s sweeping impact on growth, jobs, and prosperity 2011
  • 32 Sundararajan A. Network Effects (Arun Sundararajan, part of the Industrial Organization of Information Technology Industries web site). [date unknown] http://oz.stern.nyu.edu/io/network.html. Accessed 2015-4-11
    PubMed
  • 33 Brabham DC. Crowdsourcing as a Model for Problem Solving: An Introduction and Cases. Converg Int J Res into New Media Technol 2008; 14 (01) 75-90
    PubMedGoogle Scholar
  • 34 Brynjolfsson E, Hu Y Jeffrey, Simester D. Goodbye Pareto Principle, Hello Long Tail: The Effect of Search Costs on the Concentration of Product Sales. Manage Sci 2011; 57 (08) 1373-1386
    CrossrefPubMedGoogle Scholar
  • 35 Lavalle S, Lesser E, Shockley R, Hopkins MS, Kruschwitz N. Big Data, Analytics and the Path From Insights to Value. MIT Sloan Manag Rev 2011; 52 (02) 21-32
    PubMedGoogle Scholar
  • 36 Anderson C. The long tail: why the future of business is selling less of more. New York: Hyperion; 2006
    Google Scholar
  • 37 Rose G. Sick individuals and sick populations. Int J Epidemiol 2001; 30 (03) 427-432
    CrossrefPubMedGoogle Scholar
  • 38 Thusoo Ashish, Zheng Shao, Suresh A, Dhruba Borthakur, Namit Jain, Joydeep Sen Sarma, Raghotham Murthy, Hao Liu. Data warehousing and analytics infrastructure at facebook. In: Proceedings of the 2010 ACM SIGMOD International Conference on Management of data. ACM, 2010.. 1013-1020
  • 39 Sumbaly Roshan Kreps J, Sam Shah S.. The big data ecosystem at linkedin. In: Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data. ACM 2013: 1125-1134
    Google Scholar
  • 40 Dean J, Ghemawat S.. MapReduce: Simplied Data Processing on Large Clusters. Proc. 6th Symp Oper Syst Des Implement 2004: 137-149
    Google Scholar
  • 41 Wicks P, Vaughan TE, Massagli MP, Heywood J. Accelerated clinical discovery using self-reported patient data collected online and a patient-matching algorithm. Nat Biotechnol 2011; 29 (05) 411-414
    CrossrefPubMedGoogle Scholar
  • 42 PwC. Socio-economic impact of mHealth; An assessment report for the European Union 2013
  • 43 Swan M. Crowdsourced health research studies: an important emerging complement to clinical trials in the public health research ecosystem. J Med Internet Res 2012; 14 (02) e46
    CrossrefPubMedGoogle Scholar
  • 44 Wolf G.. The quantified self. TED@Cannes 2010
  • 45 Guyatt G, Sackett D, Taylor DW, Chong J, Roberts R, Pugsley S. Determining optimal therapy - randomized trials in individual patients. N Engl J Med 1986; 314: 889-892
    CrossrefPubMedGoogle Scholar
  • 46 Buchan I. Informatics for Healthcare Systems. In: Walshe, Kieran, Smith, Judith, editors. Healthcare Management. McGraw-Hill International 2011: 321-336
    Google Scholar
  • 47 Buchan I, Winn J, Bishop C.. A unified modeling approach to data-intensive healthcare. Fourth Paradig Data-Intensive Sci Discov Washington, DC. Microsoft Res 2009: 91-98
    Google Scholar
  • 48 Hendler J, Berners-Lee T. From the Semantic Web to social machines: A research challenge for AI on the World Wide Web. Artif Intell 2010; 174 (02) 156-161
    CrossrefPubMedGoogle Scholar
  • 49 Ainsworth J, Cunningham J, Buchan I.. ELab: Bringing together people, data and methods to enhance knowledge discovery in healthcare settings. In: Studies in Health Technology and Informatics 2012: 39-48
    Google Scholar
  • 50 McCartney M. Care.data: why are Scotland and Wales doing it differently?. BMJ 2014; 348: 1702
    CrossrefPubMedGoogle Scholar
  • 51 Beauchamp T, Childress J. Principles of Biomedical Ethics. 7th ed.. Oxford: Oxford University Press; 2012
    Google Scholar
  • 52 Open Science Collaboration. An Open, Large-Scale, Collaborative Effort to Estimate the Reproducibility of Psychological Science Perspect Psychol Sci. 2012; 7 (06) 657-660
    PubMedGoogle Scholar
  • 53 Campbell H, Hotchkiss R, Bradshaw N, Porteous M. Integrated care pathways. BMJ 1998; 316 7125 133-137
    CrossrefPubMedGoogle Scholar
  • 54 Mirnezami R, Nicholson J, Darzi A. Preparing for precision medicine. N Engl J Med 2012; 366 (06) 489-491
    CrossrefPubMedGoogle Scholar
  • 55 Kontopantelis E, Buchan I, Reeves D, Checkland K, Doran T. Relationship between quality of care and choice of clinical computing system: retrospective analysis of family practice performance under the UK’s quality and outcomes framework. BMJ Open 2013; 3 (08) e003190
    CrossrefPubMedGoogle Scholar
  • 56 Ainsworth J, Buchan I. COCPIT: a tool for integrated care pathway variance analysis. Stud Health Technol Inform 2012; 180: 995-999
    PubMedGoogle Scholar
  • 57 Brown B, Williams R, Ainsworth J, Buchan I. Missed opportunities mapping: computable healthcare quality improvement. Stud Health Technol Inform 2013; 192: 387-391
    PubMedGoogle Scholar