Next Generation Wellness: A Technology Model for Personalizing Healthcare
Personalization or individualization of care is essential to the behavioral modifications and lifestyle changes that result in patient wellness (for good health or chronic disease management). The implementation of effective personalized care is hampered by the lack of reliable means to collect and process real-time data on individual contexts (preferences, constraints) and on adherence to care protocols and mechanisms to provide timely, customized cognitive coaching that is structured, consistent and informative to users.
Provide the customized, timely, evidence/knowledge-driven messaging based on data from multiple touch points for continuous feedback to individual patients
Support this functionality within an information infrastructure of multiple service providers to provide access to unified views of patients’ data across touch points and time for multiple users (patients, providers, administrators, researchers)
Modeling of patient contexts (preferences, behaviors) within a risk-based framework
Calibration of individualized, evidence-based recommendations based on patient-generated data
Deployment of analytics functionalities within the platform model
KeywordsPersonalized healthcare Patient centered-care Data Analytics Precision Medicine Personalization Analytics Watson mobile applications Knowledge coupling with data
Many thanks to our colleagues at the IBM T.J. Watson Center and Taiwan Collaboratory who developed the earlier prototypes of the system described here.
- 1.Abbar S, Bouzeghoub M, Lopez S. Context-aware recommender systems: a service-oriented approach. In: Proceedings of the 3rd VLDB international workshop on personalized access, profile management, and context awareness in databases (VLDB PersDB Workshop, IBM Almaden, San Jose). 2009.Google Scholar
- 2.ABI Research. More than 30 billion devices will wirelessly connect to the internet of everything. 2013. Available at: https://www.abiresearch.com/press/more-than-30-billion-devices-will-wirelessly-conne.
- 3.Abrahamson M, et al. Insulin-treated type 2 diabetes: balancing physiologic and individual needs. Medscape Educ. 2006. http://www.medscape.org/viewprogram/5955 [last accessed 30 July 2015].
- 4.Adams J, Mounib E, Shabo A. IT-enabled personalized healthcare. IBM Institute for Business Value Report, Somers, NY. 2010.Google Scholar
- 5.Adomavicius G, Tuzhilin A. Context-aware recommender systems. In: Proceedings of the ACM conference on Recommender systems RecSys ‘08, Lausanne. 2008.Google Scholar
- 6.AHRQ. Medical expenditure panel survey. Rockville: Agency for Healthcare Research and Quality; 2014.Google Scholar
- 7.Apple Inc. HealthKit. 2015. URL: https://developer.apple.com/healthkit/. Last accessed 22 Apr 2015.
- 10.Butler M, Kane RL, McAlpine D, Kathol, RG, Fu SS, Hagedorn H, Wilt TJ. Integration of mental health/substance abuse and primary care no. 173 AHRQ Publication No. 09-E003, Agency for Healthcare Research and Quality. 2008.Google Scholar
- 11.Carrell SE, Hoekstra M, West JE. Is poor fitness contagious? Evidence from randomly assigned friends. National Bureau of Economic Research Working Paper No. 16518. 2010. https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0CB4QFjAA&url=http%3A%2F%2Fciteseerx.ist.psu.edu%2Fviewdoc%2Fdownload%3Fdoi%3D10.1.1.378.6489%26rep%3Drep1%26type%3Dpdf&ei=ZHE7Va6jEMnEgwTPyICwBw&usg=AFQjCNEWKhLPdSb1CevCMynelpPOS_q2xg&sig2=ilFnsgN3-YHAHLg0d7dvqg.
- 12.Census Bureau. 2012 national population projections. Washington (DC): Census Bureau. Available from: http://www.census.gov/population/projections/data/national/2012/summarytables.html.
- 13.Consumer Electronics Association (CEA) report. The Connected Health and Wellness Market. Online available at: http://www.ce.org/News/News-Releases/Press-Releases/2013-Press-Releases/CEA-Releases-Report-on-Dramatic-Rise-of-Connected.aspx. Last Access 24 Apr 2015.
- 14.Chang H, Chou PB, Ramakrishnan S. An ecosystem approach for healthcare services cloud. IEEE international conference on e-business engineering. (ICEBE ‘09, Macau, China). 2009.Google Scholar
- 17.Christensen C, Grossman J, Hwang J. The innovator’s prescription: a disruptive solution for health care. New York: McGraw-Hill; 2008.Google Scholar
- 18.CMS, Office of the Actuary, National Health Statistics Group. National health expenditures by type of sponsor: calendar years 1987–2012. Baltimore: Centers for Medicare and Medicaid Services; 2012. Available at http://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/NationalHealthExpendData/Downloads/tables.pdf.Google Scholar
- 19.CMSA (Case Management Society of America). The case management adherence guidelines (CMAG-1). 2004. Retrieved 16 Oct 2004, from http://www.cmsa.org/cmag/[Context Link].
- 22.Davenport JG, Harris TH. Competing on analytics: the new science of winning. Boston: Harvard Business School Press; 2007.Google Scholar
- 28.DPP (Diabetes Prevention Program). NIH Publication No. 09–5099, 2008, US Department of Health and Human Services. 2008.Google Scholar
- 32.FDA report. Mobile medication applications: guidance for industry and food and drug administration staff. 2015a. http://www.fda.gov/downloads/MedicalDevices/DeviceRegulationandGuidance/GuidanceDocuments/UCM263366.pdf.
- 33.FDA report. Medical device data systems, medical image storage devices, and medical image communications devices: guidance for industry and food and drug administration staff. 2015b. http://www.fda.gov/ucm/groups/fdagov-public/@fdagov-meddev-gen/documents/document/ucm401996.pdf.
- 35.Goodman C. Comparative effectiveness research and personalized medicine: from contradiction to synergy. In: Lewin Group Report prepared for the conference of comparative effectiveness research and personalized medicine: Policy, Science, and Business, National Pharmaceutical Council and Personalized Medicine Coalition, Falls Church, VA. 2009.Google Scholar
- 37.Grundy SM, et al. Detection, evaluation, and treatment of high blood cholesterol in adults (adult treatment panel III). In: The Third Report of the National Cholesterol Education Program (NCEP) expert panel. Circulation. 2002:17;106(25):3143–421.Google Scholar
- 39.Hsueh P, Lin R, Hsiao J, Zeng L, Ramakrishnan S, Chang H. Cloud-based platform for personalization in a wellness management ecosystem: why, what, and how. IEEE international conference of collaborative computing. Chicago, IL, 201.Google Scholar
- 40.Hsueh P, Lan C, Deng V, Zhu X. From clinical requirement to personalized wellness decision support: a data-driven framework for computer-supported guideline refinement. Proceedings of the 24th European Medical Informatics Conference (MIE 2012). 2012a.Google Scholar
- 41.Hsueh P, Grandison T, Zhu X, Pai H, Chang H. Challenges and requirements on privacy in enabling evidence use service on wellness cloud, frontiers in service conference. 2012b.Google Scholar
- 42.Hsueh PS, Marschollek M, Peres Y, von Cavallar S, Martin Sanchez FJ. Gap analysis of insight-driven personalized health services through patient-controlled devices. MIE 2014 Workshop, Istanbul.Google Scholar
- 45.Grandison T, Hsueh P, Zeng L, Chang H. Privacy protection issues for healthcare wellness clouds. Chapter 10. In Privacy Protection Measures and Technologies in Business Organizations (Ed. GOM Yee), IGI Global, Hershey, PA, 2011.Google Scholar
- 48.IMS Institute for health care Informatics. 2012. Press Releases. Available at: http://www.imsresearch.com/news-events/presstemplate.php?pr_id=2743.
- 49.IMS Institute for health care Informatics. Avoidable costs in U.S. health care: the $200 billion opportunity from using medicines more responsibly. 2013. Available at: http://www.imshealth.com/deployedfiles/imshealth/Global/Content/Corporate/IMS%20Institute/RUOM-2013/IHII_Responsible_Use_Medicines_2013.pdf. Accessed 10 Oct 2014.
- 51.Kansagara D, Englander H, Salanitro A, Kagen D, Theobald C, Freeman M, Kripalani S. Risk prediction models for hospital readmission: a systematic review. JAMA. 2011:19;306(15):1688–98.Google Scholar
- 55.Luga AO, McGuire MJ. Adherence and health care costs. Risk Manag Healthc Policy. 2014;7:35–44.Google Scholar
- 59.Mayo Clinics Shared Decision Making resource center. Available at http://shareddecisions.mayoclinic.org/resources/general-resources/.
- 60.Mcglynn EA, Asch SM, Kerr EA. Quality of health care delivered to adults in the United States – Reply. N Engl J Med. 2003;349:1867–8.Google Scholar
- 61.Misra V, Bhansali S, Muth J, Jackson T, Lach J. NSF Nanosystems Engineering Research Center for Advanced Self-Powered Systems of Integrated Sensors and Technologies (ASSIST). Available at: http://www.nsf.gov/awardsearch/showAward?AWD_ID=1160483&HistoricalAwards=false.
- 67.Patel S, Mancinelli C, Bonato P, Healey J, Moy M. Using wearable sensors to monitor physical activities of patients with COPD: a comparison of classifier performance. IEEE workshop on wearable and implantable body sensor networks. 2009, Berkeley, CA. p. 234–39.Google Scholar
- 68.Peikes D, Zutshi A, Genevro J, Smith K, Parchman M, Meyers D. Early evidence on the patient-centered medical home. Final report (prepared by Mathematica Policy Research, under Contract Nos. HHSA290200900019I/HHSA29032002T and HHSA290200900019I/HHSA29032005T). AHRQ Publication No. 12-0020-EF. Rockville: Agency for Healthcare Research and Quality; 2012.Google Scholar
- 69.Pierre Y. The healthcare imperative: lowering costs and improving outcomes: workshop series summary. Washington DC: The National Academies Press; 2010. p. 141–74.Google Scholar
- 70.PricewaterhouseCooper report. Consumer intelligence series: the wearable future. Online available at: http://www.pwc.com/us/en/industry/entertainment-media/publications/consumer-intelligence-series/. Last access 24 Apr 2015.
- 71.Pharmaceutical Group of the EU Staff. Targeting adherence: improving patient outcomes in Europe through community pharmacists’ intervention adherence. PGEU policy statement on adherence to medicines. 2008.Google Scholar
- 79.Schaefer C, et al. The Kaiser permanente research program on genes, environment and health: a resource for genetic epidemiology in adult health and aging. In: Proceedings of 17th annual HMO research network conference, Boston, 2011.Google Scholar
- 87.van Setten M, Pokraev S, Koolwaaij J. Context-aware recommendations in the mobile tourist application COMPASS. In: Nejdl W, De Bra P, editors. Lecture notes of computer science, vol 3137. Springer, Eindhoven, The Netherlands, 2004. p. 235–44.Google Scholar
- 89.Webb TL, Joseph J, Yardley L, Michie S. Using the internet to promote health behavior change: a systematic review and meta-analysis of the impact of theoretical basis, use of behavior change techniques, and mode of delivery on efficacy. J Med Internet Res. 2010;12(1):e4. doi: 10.2196/jmir.1376.PubMedCentralCrossRefPubMedGoogle Scholar
- 90.Welltok caféwell concierge introduction. 2015. Available at: http://welltok.com/solutions/cafewell-concierge.html.
- 91.Zimmermann A, Specht M, Lorenz A. Personalization and context management. User Model User-Adap Inter. 2006;15(3–4):275–302.Google Scholar
- 93.Stampfer MJ, Hu FB, Manson JE, RimmEB, Willett WC. Primary prevention of coronary heart disease in women through diet and lifestyle. N Engl J Med 2000;343:16-22.Google Scholar