Abstract
The objective of this paper is to define the definition of smart patients, summarize the existing foundation, and explore the approaches and system participation model of how to become a smart patient. Here a thorough review of the literature was conducted to make theory derivation processes of the smart patient; “data, information, knowledge, and wisdom (DIKW) framework” was performed to construct the model of how smart patients participate in the medical process. The smart patient can take an active role and fully participate in their own health management; DIKW system model provides a theoretical framework and practical model of smart patients; patient education is the key to the realization of smart patients. The conclusion is that the smart patient is attainable and he or she is not merely a patient but more importantly a captain and global manager of one’s own health management, a partner of medical practitioner, and also a supervisor of medical behavior. Smart patients can actively participate in their healthcare and assume higher levels of responsibility for their own health and wellness which can facilitate the development of precision medicine and its widespread practice.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Hood L, Flores M (2012) A personal view on systems medicine and the emergence of proactive P4 medicine: predictive, preventive, personalized and participatory. New Biotechnol 29(6):613–624
Soller BR et al (2002) Smart medical systems with application to nutrition and fitness in space. Nutrition 18(10):930–936
Giovanni Acampora DJC, Rashidi P, Vasilakos AV (2013) A survey on ambient intelligence in health care. Proc IEEE Inst Electr Electron Eng 101(12):2470–2494
Kartakis S et al (2012) Enhancing health care delivery through ambient intelligence applications. Sensors (Basel) 12(9):11435–11450
van der Werf CS et al (2015) Congenital short bowel syndrome: from clinical and genetic diagnosis to the molecular mechanisms involved in intestinal elongation. Biochim Biophys Acta 1852(11):2352–2361
Ona T, Shibata J (2010) Advanced dynamic monitoring of cellular status using label-free and non-invasive cell-based sensing technology for the prediction of anticancer drug efficacy. Anal Bioanal Chem 398(6):2505–2533
Chen J et al (2013) Translational biomedical informatics in the cloud: present and future. Biomed Res Int 2013:658925
Bahcall O (2015) Precision medicine. Nature 526(7573):335
Roizen MF, Oz MC (2006) You the smart patient: an insider’s handbook for getting the best treatment. Free Press, New York
Zengota EG (1986) Planning a “smart” patient security system. Contemp Longterm Care 9(8):30. 32
Seidman S (1990) Press release: European community to use smart patient cards. J Med Syst 14(3):158–159
Park CS et al (2011) Development and evaluation of “hospice smart patient” service program. J Korean Acad Nurs 41(1):9–17
Kim YM, Bazant E, Storey JD (2006) Smart patient, smart community: improving client participation in family planning consultations through a community education and mass-media program in Indonesia. Int Q Community Health Educ 26(3):247–270
Hoo WE (2006) On “smart” patients as consumers. J Healthc Qual 28(6):4. 12
Hogan NM, Kerin MJ (2012) Smart phone apps: smart patients, steer clear. Patient Educ Couns 89(2):360–361
Abdaoui A et al (2015) E-patient reputation in health forums. Stud Health Technol Inform 216:137–141
Gee PM et al (2012) Exploration of the e-patient phenomenon in nursing informatics. Nurs Outlook 60(4):e9–16
Gee PM et al (2015) E-patients perceptions of using personal health records for self-management support of chronic illness. Comput Inform Nurs 33(6):229–237
Meehan TP (2014) Transforming patient to partner: the e-patient movement is a call to action. Conn Med 78(3):175–176
Cook DA et al (2015) A comprehensive information technology system to support physician learning at the point of care. Acad Med 90(1):33–39
Smith PF, Ross DA (2012) Information, knowledge, and wisdom in public health surveillance. J Public Health Manag Pract 18(3):193–195
Herr TM et al (2015) A conceptual model for translating omic data into clinical action. J Pathol Inform 6:46
Dorajoo R, Liu J, Boehm BO (2015) Genetics of type 2 diabetes and clinical utility. Genes (Basel) 6(2):372–384
Hebbring SJ (2014) The challenges, advantages and future of phenome-wide association studies. Immunology 141(2):157–165
Pendergrass SA et al (2011) The use of phenome-wide association studies (PheWAS) for exploration of novel genotype-phenotype relationships and pleiotropy discovery. Genet Epidemiol 35(5):410–422
Pendergrass SA et al (2013) Phenome-wide association study (PheWAS) for detection of pleiotropy within the population architecture using genomics and epidemiology (PAGE) network. PLoS Genet 9(1):e1003087
Denny JC et al (2013) Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data. Nat Biotechnol 31(12):1102–1110
Denny JC et al (2010) PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene-disease associations. Bioinformatics 26(9):1205–1210
Anand P et al (2008) Cancer is a preventable disease that requires major lifestyle changes. Pharm Res 25(9):2097–2116
Gorelik GJ, Yarlagadda S, Richardson BC (2012) PKCδ oxidation contributes to ERK inactivation in lupus t CELLS1. Arthritis Rheum 64(9):2964–2974
Romani M, Pistillo MP, Banelli B (2015) Environmental epigenetics: crossroad between public health, lifestyle, and cancer prevention. Biomed Res Int 2015:587983
Huser V, Sincan M, Cimino JJ (2014) Developing genomic knowledge bases and databases to support clinical management: current perspectives. Pharmgenomics Pers Med 7:275–283
Mirnezami R, Nicholson J, Darzi A (2012) Preparing for precision medicine. N Engl J Med 366(6):489–491
Ibrahim A et al (2015) Case study for integration of an oncology clinical site in a semantic interoperability solution based on HL7 v3 and SNOMED-CT: data transformation needs. AMIA Jt Summits Transl Sci Proc 2015:71
Omidi Y (2011) Smart multifunctional theranostics: simultaneous diagnosis and therapy of cancer. Bioimpacts 1(3):145–147
Wang J et al (2014) Smartphone interventions for long-term health management of chronic diseases: an integrative review. Telemed J E Health 20(6):570–583
Boulos MNK et al (2011) How smartphones are changing the face of mobile and participatory healthcare: an overview, with example from eCAALYX. Biomed Eng Online 10:24
Free C et al (2013), The effectiveness of mobile-health technology-based health behaviour change or disease management interventions for health care consumers: a systematic review. PLoS Med 10(1)
Mosa ASM, Yoo I, Sheets L (2012) A systematic review of healthcare applications for smartphones. BMC Med Inform Decis Mak 12:67
Pope L, Silva P, Almeyda R 2010 I-phone applications for the modern day otolaryngologist. Clin Otolaryngol 35(4):350–354
Pramana G et al (2014) The SmartCAT: an m-health platform for ecological momentary intervention in child anxiety treatment. Telemed J E Health 20(5):419–427
Yu F et al (2012) A smartphone application of alcohol resilience treatment for behavioral self-control training. Conf Proc IEEE Eng Med Biol Soc 2012:1976–1979
Bhat S et al (2015) Is there a clinical role for smartphone sleep apps? Comparison of sleep cycle detection by a smartphone application to polysomnography. J Clin Sleep Med 11(7):709–715
Becker S et al (2015) Demographic and health related data of users of a mobile application to support drug adherence is associated with usage duration and intensity. PLoS One 10(1):e0116980
Becker S et al (2013) User profiles of a smartphone application to support drug adherence – experiences from the iNephro project. PLoS One 8(10):e78547
Kanawong R et al (2012) Automated tongue feature extraction for ZHENG classification in traditional Chinese medicine. Evid Based Complement Alternat Med 2012:912852
Robbins RN et al (2014) A smartphone app to screen for HIV-related neurocognitive impairment. J Mob Technol Med 3(1):23–26
Bajaj JS et al (2013) The Stroop smartphone application is a short and valid method to screen for minimal hepatic encephalopathy. Hepatology 58(3):1122–1132
Sposaro F, Tyson G (2009) iFall: an android application for fall monitoring and response. Conf Proc IEEE Eng Med Biol Soc 2009:6119–6122
Tarbert CM, Livingstone IA, Weir AJ (2014) Assessment of visual impairment in stroke survivors. Conf Proc IEEE Eng Med Biol Soc 2014:2185–2188
Park JY et al (2014) Lessons learned from the development of health applications in a tertiary hospital. Telemed J E Health 20(3):215–222
Agboola S, Kamdar M (2014) Pain management in cancer patients using a mobile app: study design of a randomized controlled trial. JMIR Res Protoc 3(4):e76
Cafazzo JA et al (2015) Usability and feasibility of an mHealth intervention for monitoring and managing pain symptoms in sickle cell disease: the sickle cell disease mobile application to record symptoms via technology (SMART). J Med Internet Res 39(3):162–168
Charpentier G et al (2011) The Diabeo software enabling individualized insulin dose adjustments combined with telemedicine support improves HbA1c in poorly controlled type 1 diabetic patients: a 6-month, randomized, open-label, parallel-group, multicenter trial (TeleDiab 1 study). Diabetes Care 34(3):533–539
Worringham C, Rojek A, Stewart I (2011) Development and feasibility of a smartphone, ECG and GPS based system for remotely monitoring exercise in cardiac rehabilitation. PLoS One 6(2):e14669
Marshall A, Medvedev O, Antonov A (2008) Use of a smartphone for improved self-management of pulmonary rehabilitation. Int J Telemed Appl: p 753064
Ryan D et al (2005) Mobile phone technology in the management of asthma. J Telemed Telecare 11(Suppl 1):43–46
Atreja A, Khan S (2015) Impact of the mobile Health Promise platform on the quality of care and quality of life in patients with inflammatory bowel disease: study protocol of a pragmatic randomized controlled trial. JMIR Res Protoc 4(1): e23
Bangsberg DR, Pan D, Dhall R (2015) A mobile cloud-based Parkinson's disease assessment system for home-based monitoring. J Med Internet Res 3(1):e29
Bosl W et al (2013) Scalable decision support at the point of care: a substitutable electronic health record app for monitoring medication adherence. Interact J Med Res 2(2):e13
Cho MJ, Sim JL, Hwang SY (2014) Development of smartphone educational application for patients with coronary artery disease. Healthc Inform Res 20(2):117–124
Franckle T, Haas D, Mandl KD (2013) App store for EHRs and patients both. AMIA Jt Summits Transl Sci Proc 2013:73
Goh G, Tan NC (2015) Short-term trajectories of use of a caloric-monitoring mobile phone app among patients with type 2 diabetes mellitus in a primary care setting. J Med Internet Res 17(2):e33
Csernansky JG, Smith MJ (2011) Thought, feeling, and action in real time – monitoring of drug use in schizophrenia. Am J Psychiatry 168(2):120–122
Swendsen J, Ben-Zeev D, Granholm E (2011) Real-time electronic ambulatory monitoring of substance use and symptom expression in schizophrenia. Am J Psychiatry 168(2):202–209
Sands BE et al (2015) Feasibility of a lifestyle intervention for overweight/obese endometrial and breast cancer survivors using an interactive mobile application. JMIR Res Protoc 137(3):508–515
Carter MC et al (2013) Adherence to a smartphone application for weight loss compared to website and paper diary: pilot randomized controlled trial. J Med Internet Res 15(4):e32
Casey M et al (2014) Patients’ experiences of using a smartphone application to increase physical activity: the SMART MOVE qualitative study in primary care. Br J Gen Pract 64(625):e500–e508
Dafli E, Antoniou P (2015) Virtual patients on the semantic Web: a proof-of-application study. J Med Internet Res 17(1):e16
Ward MM et al (2003) Participatory patient-physician communication and morbidity in patients with systemic lupus erythematosus. Arthritis Rheum 49(6):810–818
Durand MA et al (2014) Do interventions designed to support shared decision-making reduce health inequalities? A systematic review and meta-analysis. PLoS One 9(4):e94670
Goddu AP, Raffel KE, Peek ME (2015) A story of change: the influence of narrative on African-Americans with diabetes. Patient Educ Couns 98(8):1017–1024
Lejbkowicz I, Caspi O, Miller A (2012) Participatory medicine and patient empowerment towards personalized healthcare in multiple sclerosis. Expert Rev Neurother 12(3):343–352
Majmudar MD, Colucci LA, Landman AB (2015) The quantified patient of the future: opportunities and challenges. Healthc (Amst) 3(3):153–156
Call J et al (2012) Survival of gastrointestinal stromal tumor patients in the imatinib era: life raft group observational registry. BMC Cancer 12:90
Kear T, Harrington M, Bhattacharya A (2015) Partnering with patients using social media to develop a hypertension management instrument. J Am Soc Hypertens 9(9):725–734
Hood L, Auffray C (2013) Participatory medicine: a driving force for revolutionizing healthcare. Genome Med 5(12):110
Palmer JE (2012) Genetic gatekeepers: regulating direct-to-consumer genomic services in an era of participatory medicine. Food Drug Law J 67(4):475–524. iii
Reeves S et al (2017) Interprofessional collaboration to improve professional practice and healthcare outcomes. Cochrane Database Syst Rev, CD000072.pub3
Jain M et al (2006) Decline in ICU adverse events, nosocomial infections and cost through a quality improvement initiative focusing on teamwork and culture change. Qual Saf Health Care 15(4):235–239
Almalki M, Gray K, Sanchez FM (2015) The use of self-quantification systems for personal health information: big data management activities and prospects. Health Inf Sci Syst 3(Suppl 1. HISA Big Data in Biomedicine and Healthcare 2013 Con):S1
Kuziemsky C et al (2014) A framework for incorporating patient preferences to deliver participatory medicine via interdisciplinary healthcare teams. AMIA Annu Symp Proc 2014:835–844
Bredfeldt C et al (2015) Patient reported outcomes for diabetic peripheral neuropathy. J Diabetes Complications 29(8):1112–1118
Frost J et al (2011) Patient-reported outcomes as a source of evidence in off-label prescribing: analysis of data from PatientsLikeMe. J Med Internet Res 13(1):e6
Norris K (2014) Lung cancer patient advocacy and participatory medicine. Genome Med 6(1):7
Charani E et al (2014) Do smartphone applications in healthcare require a governance and legal framework? It depends on the application! BMC Med 12:29
Acknowledgments
This study was supported by the National Natural Science Foundation of China (NSFC) (grant nos. 31670851, 31470821, and 91530320) and National Key R&D programs of China (2016YFC1306605).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Chen, Y., Yang, L., Hu, H., Chen, J., Shen, B. (2017). How to Become a Smart Patient in the Era of Precision Medicine?. In: Shen, B. (eds) Healthcare and Big Data Management. Advances in Experimental Medicine and Biology, vol 1028. Springer, Singapore. https://doi.org/10.1007/978-981-10-6041-0_1
Download citation
DOI: https://doi.org/10.1007/978-981-10-6041-0_1
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-6040-3
Online ISBN: 978-981-10-6041-0
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)