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CC BY-NC-ND 4.0 · Yearb Med Inform 2019; 28(01): 181-189
DOI: 10.1055/s-0039-1677916
Section 8: Bioinformatics and Translational Informatics
Survey
Georg Thieme Verlag KG Stuttgart

Personalized Medicine Implementation with Non-traditional Data Sources: A Conceptual Framework and Survey of the Literature

Casey Overby Taylor
1   Department of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
,
Peter Tarczy-Hornoch
2   Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, Washington, USA
› Author Affiliations
Further Information

Publication History

Publication Date:
16 August 2019 (online)

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Summary

Objectives: With the explosive growth in availability of health data captured using non-traditional sources, the goal for this work was to evaluate the current biomedical literature on theory- driven studies investigating approaches that leverage non- traditional data in personalized medicine applications.

Methods: We conducted a literature assessment guided by the personalized medicine unsolicited health information (pUHl) conceptual framework incorporating diffusion of innovations and task-technology fit theories.

Results: The assessment provided an oveiview of the current literature and highlighted areas for future research. In particular, there is a need for: more research on the relationship between attributes of innovation and of societal structure on adoption; new study designs to enable flexible communication channels; more work to create and study approaches in healthcare settings; and more theory-driven studies with data-driven interventions.

Conclusion: This work introduces to an informatics audience an elaboration on personalized medicine implementation with non-traditional data sources by blending it with the pUHl conceptual framework to help explain adoption. We highlight areas to pursue future theory-driven research on personalized medicine applications that leverage non-traditional data sources.

Keywords

Personalized medicine - implementation science - dissemination research - diffusion of innovation - review literature as topic
 

  • References

  • 1 Allen CG, Andersen B, Khoury MJ, Roberts MC. Current Social Media Conversations about Genetics and Genomics in Health: A Twitter-Based Analysis. Public Health Genomics 2018; 21 (1-2): 93-9
    CrossrefPubMedGoogle Scholar
  • 2 Ross E, Swallow J, Kerr A, Cunningham-Burley S. Online accounts of gene expression profiling in early-stage breast cancer: Interpreting genomic testing for chemotherapy decision making. Health Expect 2019; 22 (01) 74-82
    CrossrefPubMedGoogle Scholar
  • 3 Piwek L, Ellis DA, Andrews S, Joinson A. The Rise of Consumer Health Wearables: Promises and Barriers. PLoS Med 2016; 13 (02) e1001953
    CrossrefPubMedGoogle Scholar
  • 4 Riley WT, Nilsen WJ, Manolio TA, Masys DR, Lauer M. News from the NIH: potential contributions of the behavioral and social sciences to the precision medicine initiative. Transl Behav Med 2015; 5 (03) 243-6
    CrossrefPubMedGoogle Scholar
  • 5 Torkamani A, Andersen KG, Steinhubl SR, Topol EJ. High-Definition Medicine. Cell 2017; 170 (05) 828-43
    CrossrefPubMedGoogle Scholar
  • 6 Lautenbach DM, Christensen KD, Sparks JA, Green RC. Communicating genetic risk information for common disorders in the era of genomic medicine. Annual Rev Genomics Hum Genet 2013; 14: 491-513
    PubMedGoogle Scholar
  • 7 Kullo IJ, Jouni H, Austin EE, Brown SA, Kruisselbrink TM, Isseh IN. , et al. Incorporating a Genetic Risk Score Into Coronary Heart Disease Risk Estimates: Effect on Low-Density Lipoprotein Cholesterol Levels (the MI-GENES Clinical Trial). Circulation 2016; 133 (12) 1181-8
    CrossrefPubMedGoogle Scholar
  • 8 Zhu H, Colgan J, Reddy M, Choe EK. Sharing Patient-Generated Data in Clinical Practices: An Interview Study. AMIA Annu Symp Proc 2016; 2016: 1303-12
    PubMedGoogle Scholar
  • 9 Cohen DJ, Keller SR, Hayes GR, Dorr DA, Ash JS, Sittig DF. Integrating Patient-Generated Health Data Into Clinical Care Settings or Clinical Decision-Making: Lessons Learned From Project HealthDesign. JMIR Hum Factors 2016; 3 (02) e26
    CrossrefPubMedGoogle Scholar
  • 10 2017 AMIA Policy Invitational. Redefining Our Picture of Health: Towards a Person-Centered Integrated Care, Research, Wellness, and Community Ecosystem. Available from: https://www.amia.org/sites/default/files/API-2017-White-Paper-Redefining-our-Picture-of-Health.pdf [Accessed 11 Feburary 2019]
    PubMed
  • 11 Haendel MA, Chute CG, Robinson PN. Classification, Ontology, and Precision Medicine. N Engl J Med 2018; 379 (15) 1452-62
    CrossrefPubMedGoogle Scholar
  • 12 Weiss D, Rydland HT, Oversveen E, Jensen MR, Solhaug S, Krokstad S. Innovative technologies and social inequalities in health: A scoping review of the literature. PloS One 2018; 13 (04) e0195447
    CrossrefPubMedGoogle Scholar
  • 13 Hollister B, Bonham VL. Should Electronic Health Record-Derived Social and Behavioral Data Be Used in Precision Medicine Research?. AMA J Ethics 2018; 20 (09) E873-80
    PubMedGoogle Scholar
  • 14 De La Vega FM, Bustamante CD. Polygenic risk scores: a biased prediction?. Genome Med 2018; 10 (01) 100
    CrossrefPubMedGoogle Scholar
  • 15 Khoury MJ, Gwinn M, Yoon PW, Dowling N, Moore CA, Bradley L. The continuum of translation research in genomic medicine: how can we accelerate the appropriate integration of human genome discoveries into health care and disease prevention?. Genet Med 2007; 9 (10) 665-74
    PubMedGoogle Scholar
  • 16 Maass W, Parsons J, Purao S, Storey VC, Woo C. Data-driven meets theory-driven research in the era of big data: opportunities and challenges for information systems research. Journal of the Association for Information Systems 2018; 19 (12) 1253-73
    PubMedGoogle Scholar
  • 17 Chambers DA, Feero WG, Khoury MJ. Convergence of Implementation Science, Precision Medicine, and the Learning Health Care System: A New Model for Biomedical Research. JAMA 2016; 315 (18) 1941-2
    CrossrefPubMedGoogle Scholar
  • 18 Dearing JW. Applying Diffusion of Innovation Theory to Intervention Development. Res Soc Work Pract 2009; 19 (05) 503-18
    CrossrefPubMedGoogle Scholar
  • 19 Davis F. User acceptance of information technology: system characteristics, user perceptions and behavioral impacts. Int J Man Mach Stud 1993; 38 (03) 475-87
    CrossrefPubMedGoogle Scholar
  • 20 Tavares J, Oliveira T. Electronic Health Record Patient Portal Adoption by Health Care Consumers: An Acceptance Model and Survey. J Med Internet Res 2016; 18 (03) e49
    CrossrefPubMedGoogle Scholar
  • 21 Rahimi B, Nadri H, Lotfoezhad Afshar H, Timpka T. A Systematic Review of the Technology Acceptance Model in Health Informatics. Appl Clin Inform 2018; 9 (03) 604-34
    Article in Thieme ConnectPubMedGoogle Scholar
  • 22 Holden RJ, Karsh BT. The technology acceptance model: its past and its future in health care. J Biomed Inform 2010; 43 (01) 159-72
    CrossrefPubMedGoogle Scholar
  • 23 Moore GC, Benbasat I. Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research 1991; 2 (03) 192-222
    CrossrefPubMedGoogle Scholar
  • 24 Overby CL, Devine EB, Abernethy N, McCune JS, Tarczy-Hornoch P. Making pharmacogenomic-based prescribing alerts more effective: A scenario-based pilot study with physicians. J Biomed Inform 2015; 55: 249-59
    CrossrefPubMedGoogle Scholar
  • 25 Rogers EM. Diffusion of innovations. Simon and Schuster; 2010
    PubMed
  • 26 Goodhue DL, Thompson RL. Task-technology fit and individual performance. MIS Q 1995;213-36
    PubMed
  • 27 Massey AP, Montoya-Weiss M, Hung C, Ramesh V. Cultural perceptions of task-technology fit. Communications of the ACM 2001; 44 (12) 83-4
    PubMedGoogle Scholar
  • 28 PubMed Clinical Queries Filters [Internet]. Available from: https://www.ncbi.nlm.nih.gov/books/NBK3827/#pubmedhelp.Clinical_Queries_Filters . Secondary PubMed Clinical Queries Filters [Internet]. Available from: https://www ncbi.nlm.nih.gov/books/NBK3827/#pubmedhelp . Clinical_Queries_Filters 2005
    PubMed
  • 29 Strekalova YA, Hawkins KE, Drusbosky LM, Cogle CR. Using social media to assess care coordination goals and plans for leukemia patients and survivors. Transl Behav Med 2018; 8 (03) 481-91
    CrossrefPubMedGoogle Scholar
  • 30 Ahmed S, Hayward J, Ahmed M. Primary care professionals’ perceptions of using a short family history questionnaire. Family Pract 2016; 33 (06) 704-08
    CrossrefPubMedGoogle Scholar
  • 31 Bodurtha JN, McClish D, Gyure M, Corona R, Krist AH, Rodriguez VM. , et al. The KinFact intervention - a randomized controlled trial to increase family communication about cancer history. J Womens Health (Larchmt) 2014; 23 (10) 806-16
    CrossrefPubMedGoogle Scholar
  • 32 Lewis C, Hill M, Skirton H, Chitty LS. Development and validation of a measure of informed choice for women undergoing non-invasive prenatal testing for aneuploidy. Eur J Hum Genet 2016; 24 (06) 809-16
    CrossrefPubMedGoogle Scholar
  • 33 Patrick-Miller LJ, Egleston BL, Fetzer D, Forman A, Bealin L, Rybak C. , et al. Development of a communication protocol for telephone disclosure of genetic test results for cancer predisposition. JMIR Res Protoc 2014; 3 (04) e49
    CrossrefPubMedGoogle Scholar
  • 34 Pruppers MH, Merkies IS, Faber CG, Da Silva A, Costa V, Coelho T. The Val30Met familial amyloid polyneuropathy specific Rasch-built overall disability scale (FAP-RODS((c))). J Peripher Nerv Syst 2015; 20 (03) 319-27
    CrossrefPubMedGoogle Scholar
  • 35 Seaborn C, Suther S, Lee T, Kiros GE, Becker A, Campbell E. , et al. Utilizing Genomics through Family Health History with the Theory of Planned Behavior: Prediction of Type 2 Diabetes Risk Factors and Preventive Behavior in an African American Population in Florida. Public Health Genomics 2016; 19 (02) 69-80
    CrossrefPubMedGoogle Scholar
  • 36 Lowery JT, Horick N, Kinney AY, Finkelstein DM, Garrett K, Haile RW. , et al. A randomized trial to increase colonoscopy screening in members of high-risk families in the colorectal cancer family registry and cancer genetics network. Cancer Epidemiol Biomarkers Prev 2014; 23 (04) 601-10
    CrossrefPubMedGoogle Scholar
  • 37 Weitzel KW, Alexander M, Bernhardt BA, Caiman N, Carey DJ, Cavallari LH. , et al; IGNITE Network. The IGNITE network: a model for genomic medicine implementation and research. BMC Med Genomics 2016; 9: 1
    PubMedGoogle Scholar
  • 38 Damschroder LJ, Aron DC, Keith RE, Kirsh SR, Alexander JA, Lowery JC. Fostering implementation of health services research findings into practice: a consolidated framework for advancing implementation science. Implement Sci 2009; 4: 50
    CrossrefPubMedGoogle Scholar
  • 39 Orlando LA, Sperber NR, Voils C, Nichols M, Myers RA, Wu RR. , et al. Developing a common framework for evaluating the implementation of genomic medicine interventions in clinical care: the IGNITE Network’s Common Measures Working Group. Genet Med 2018; 20 (06) 655-63
    PubMedGoogle Scholar
  • 40 Sperber NR, Carpenter JS, Cavallari LHJ, Damschroder L, Cooper-DeHoff RM, Denny JC. , et al. Challenges and strategies for implementing genomic services in diverse settings: experiences from the Implementing GeNomics In pracTicE (IGNITE) network. BMC Med Genomics 2017; 10 (01) 35
    CrossrefPubMedGoogle Scholar
  • 41 Varabyova Y, Blankart CR, Greer AL, Schreyogg J. The determinants of medical technology adoption in different decisional systems: A systematic literature review. Health Policy 2017; 121 (03) 230-42
    CrossrefPubMedGoogle Scholar
  • 42 Walter Z, Lopez MS. Physician acceptance of information technologies: Role of perceived threat to professional autonomy. Decis Support Syst 2008; 46 (01) 206-15
    CrossrefPubMedGoogle Scholar
  • 43 Angst CM, Agarwal R. Adoption of electronic health records in the presence of privacy concerns: The elaboration likelihood model and individual persuasion. MIS Q 2009; 33 (02) 339-70
    CrossrefPubMedGoogle Scholar
  • 44 Schully SD, Khoury MJ. What is translational genomics? An expanded research agenda for improving individual and population health. Appl Transl Genom 2014; 3 (04) 82-83
    PubMedGoogle Scholar
  • 45 Clyne M, Schully SD, Dotson WD, Douglas MP, Gwinn M, Kolor K. , et al. Horizon scanning for translational genomic research beyond bench to bedside. Genetics in medicine : official journal of the American College of Medical Genetics 2014; 16 (07) 535-8
    CrossrefPubMedGoogle Scholar
  • 46 Roberts MC, Kennedy AE, Chambers DA, Khoury MJ. The current state of implementation science in genomic medicine: opportunities for improvement. Genet Medic 2017; 19 (08) 858
    PubMedGoogle Scholar
  • 47 Rahimi B, Vimarlund V. Methods to evaluate health information systems in healthcare settings: a literature review. J Med Syst 2007; 31 (05) 397-432
    CrossrefPubMedGoogle Scholar
  • 48 Yu KH, Kohane IS. Framing the challenges of artificial intelligence in medicine. BMJ Qual Saf 2019; 28 (03) 238-41
    CrossrefPubMedGoogle Scholar
  • 49 Richter AN, Khoshgoftaar TM. A review of statistical and machine learning methods for modeling cancer risk using structured clinical data. Artif Intell Med 2018; 90: 1-14
    CrossrefPubMedGoogle Scholar
  • 50 Collins FS, Varmus H. A new initiative on precision medicine. N Engl J Med 2015; 372 (09) 793-5
    CrossrefPubMedGoogle Scholar
  • 51 Context: The missing piece in the machine learning lifecycle. KDD CMI Workshop; 2018
    PubMed
  • 52 IMAGene: A study for healthy volunteers evaluating how genes affect health risks. Available from: https://recruit.cumc.columbia.edu/clinical_trial/1045 . Secondary IMAGene: A study for healthy volunteers evaluating how genes affect health risks. Available from: https://recruit.cumc.columbia.edu/clinical_trial/1045 2017
    PubMed
  • 53 Yen PY, McAlearney AS, Sieck CJ, Hefner JL, Huerta TR. Health Information Technology (HIT) Adaptation: Refocusing on the Journey to Successful HIT Implementation. JMIR Med Inform 2017; 5 (03) e28
    CrossrefPubMedGoogle Scholar
  • 54 Welch BM, Kawamoto K. Clinical decision support for genetically guided personalized medicine: a systematic review. J Am Med Inform Assoc 2013; 20 (02) 388-400
    CrossrefPubMedGoogle Scholar
  • 55 O’Donnell A, Kaner E, Shaw C, Haighto C. Primary care physicians’ attitudes to the adoption of electronic medical records: a systematic review and evidence synthesis using the clinical adoption framework. BMC Medi Inform Decis Mak 2018; 18 (01) 101
    PubMedGoogle Scholar
  • 56 Haga B, Barry WT, Mills R, Svetkey L, Suchindran S, Willard HF. , et al. Impact of delivery models on understanding genomic risk for type 2 diabetes. Public Health Genomics 2014; 17 (02) 95-104
    CrossrefPubMedGoogle Scholar
  • 57 Persky S, Kistler WD, Klein WMP, Ferrer RA. Internet Versus Virtual Reality Settings for Genomics Information Provision. Cyberpsychol Behav Soc Netw 2019; 22 (01) 7-14
    CrossrefPubMedGoogle Scholar
  • 58 Birch PH. Interactive e-counselling for genetics pre-test decisions: where are we now?. Clin Genet 2015; 87 (03) 209-17
    CrossrefPubMedGoogle Scholar
  • 59 Birch P, Adam S, Bansback N, Coe RR, Hicklin J, Lehman A. , et al. DECIDE: a Decision Support Tool to Facilitate Parents’ Choices Regarding Genome-Wide Sequencing. J Genet Couns 2016; 25 (06) 1298-308
    CrossrefPubMedGoogle Scholar
  • 60 Phillips KA, Deverka PA, Sox HC, Khoury MJ, Sandy LG, Ginsburg GS. , et al. Making genomic medicine evidence-based and patient-centered: a structured review and landscape analysis of comparative effectiveness research. Genet Med 2017; 19 (10) 1081-91
    PubMedGoogle Scholar
  • 61 Hamilton JG, Abdiwahab E, Edwards HM, Fang ML, Jdayani A, Breslau ES. Primary care providers’ cancer genetic testing-related knowledge, attitudes, and communication behaviors: A systematic review and research agenda. J Gen Intern Med 2017; 32 (03) 315-24
    CrossrefPubMedGoogle Scholar
  • 62 Rohrer Vitek CR, Abul-Husn NS, Connolly JJ, Hartzler AL, Kitchner T, Peterson JF. , et al. Healthcare provider education to support integration of pharmacogenomics in practice: the eMERGE Network experience. Pharmacogenomics 2017; 18 (10) 1013-25
    CrossrefPubMedGoogle Scholar
  • 63 Schmidlen T, Sturm AC, Hovick S, Scheinfeldt L, Scott Roberts J, Morr L. , et al. Operationalizing the Reciprocal Engagement Model of Genetic Counseling Practice: a Framework for the Scalable Delivery of Genomic Counseling and Testing. J Genet Couns 2018; 27 (05) 1111-29
    CrossrefPubMedGoogle Scholar
  • 64 Green RC, Goddard KA, Jarvik GP, Amendola LM, Appelbaum PS, Berg JS. , et al; Clinical Sequencing Exploratory Research Consortium. Accelerating Evidence-Based Practice of Genomic Medicine. Am J Hum Genet 2016; 99 (01) 246
    PubMedGoogle Scholar
  • 65 de Brun T, de Brun MOR, van Weel-Baumgarten E, van Weel C, Dowrick C, Lionis C. , et al. Guidelines and training initiatives that support communication in cross-cultural primary-care settings: appraising their implementability using Normalization Process Theory. Fam Pract 2015; 32 (04) 420-25
    PubMedGoogle Scholar
  • 66 May CR, Cummings A, Girling M, Bracher M, Mair FS, May CM. , et al. Using Normalization Process Theory in feasibility studies and process evaluations of complex healthcare interventions: a systematic review. Implement Sci 2018; 13 (01) 80
    CrossrefPubMedGoogle Scholar
  • 67 May C, Finch TJS. Implementing, embedding, and integrating practices: an outline of normalization process theory. Sociology 2009; 43 (03) 535-54
    PubMedGoogle Scholar
  • 68 Marteau TM, Dormandy E, Michie S. A measure of informed choice. Health Expect 2001; 4 (02) 99-108
    CrossrefPubMedGoogle Scholar
  • 69 Michie S, Dormandy E, Marteau TM. The multi-dimensional measure of informed choice: a validation study. Patient Educ Couns 2002; 48 (01) 87-91
    CrossrefPubMedGoogle Scholar
  • 70 Leventhal H, Diefenbach M, Leventhal EA. Illness cognition: using common sense to understand treatment adherence and affect cognition interactions. Cognit Ther Res 1992; 16 (02) 143-63
    CrossrefPubMedGoogle Scholar
  • 71 Robitaille C. Illness representations: theoretical foundations. Perceptions of health illness: Current research applications; 1997
    PubMed
  • 72 Andrich D, Sheridan B, Luo G. Rasch models for measurement: RUMM2030. Perth, Western Australia: RUMM Laboratory Pty Ltd; 2010
    PubMed
  • 73 Rasch G. Probabilistic models for some intelligence and attainment tests. Copenhagen: Danish Institute for Educational Research; 1960
    PubMed
  • 74 Ajzen I, Fishbein M. Understanding attitudes and predicting social behaviour. 1980
    PubMed
  • 75 Berger CR. Planning theory of communication. Sage Publications, Inc; 2008
    Google Scholar