The Promise of Small Data for Telemedicine in Chronic Condition Management: A Real-World Case Series
DOI:
https://doi.org/10.17140/CTPOJ-4-117Keywords:
Telemedicine, Small data, n-of-1, Internet of things, Chronic conditions, Self-management, Predictive analyticsAbstract
Connected care is defined as the “real-time, electronic communication between a patient and a provider, including telehealth, remote patient monitoring,
and secure email communication between clinicians and their patients” (Alliance of Connected Care). Connected care can create a
high-value interaction strategy with patients when it makes thoughtful use of commercially available digital health technologies
with demonstrated both clinical and economic effectiveness. Karantis360™, is a home sensor technology that enables real-time
tracking, data analytics and predictive care for personal (at home) care powered by IBM Watson Health. IndividuALLyticsTM is a
telemedicine platform driven by a patent-pending an N-of-1 analytical engine and related digital dashboards that provides individual,
patient-level evaluation of treatment response. The underlying technology combines disparate digital health technology
data with the best evidence-base guidelines with N-of-1 methodology. The output allows for creation of personalized treatments
empirically tested at the patient-level over time (aka over the course of care). When aggregated both within and across persons,
the time-ordered data can build predictive pathways of behavior and ensure the relevant care and medical treatments are in place
to support effective medical and self-management of chronic illness. This case-series report describes the implementation of a
joint home sensor technology (big data) and an N-of-1 analytic engine (small data) with three elderly consented volunteer customers-
patients of Karantis360™. Each person underwent successive, 2-week behavioral change treatment phases to determine
usability, utility regarding medical and self-management and any proximal effects on health risks.
References
David JW, Chung R, Juarez DT. Prevalence of comorbid condi-tions with aging among patients with diabetes and cardiovascular 8 Schwartz SM et al disease. Hawaii Med J. 2011; 70(10): 209-213.
Divo, MJ, Martinez, CH, Mannino, DM. Ageing and the epide-miology of multimorbidity. Eur Respir. 2014; 44(4): 1055-1068. doi: 10.1183/09031936.00059814
World Health Organization (WHO). Ageing and health. 2018. Web site. https://www.who.int/news-room/fact-sheets/detail/ ageing-and-health/. Accessed November 17, 2020.
Mathers CD, Loncar D. Projections of global mortality and bur-den of disease from 2002 to 2030. PLoS Med. 2006; 3(11): e442. doi: 10.1371/journal.pmed.0030442
Kim, EJ, Jung, SW, Kim, YE. Assessing the impact of aging on burden of disease. Iran J of Public Health. 2018; 47(1): 33-58.
Blinman PL, Grimson P, Barton MB, Crossing S, Walpole ET, Wong N, et al. The shortage of medical oncologists: The Austral-ian Medical Oncologist workforce study. Med J Aust. 2012; 196(1): 58-62. doi: 10.5694/mja11.10363
Dall TM, Storm, MV, Chakrabarti R, Drogan O, Keran CM, Donofrio PD, et al. Supply and demand analysis of the current and future neurology workforce. Neurology. 2013; 81(5): 470-480. doi: 10.1212/WNL.0b013e318294b1cf
Ostbye t, Yarnall KSH, Krause KM, Pollak KI, Gradison M, Michener JL. Is there time for management of patients with chron-ic diseases in primary care? Ann Fam Med. 2005; 3: 209-214. doi: 10.1370/afm.310
Petterson SM, Liaw WR, Phillips RL. Projecting US primary care physician workforce needs: 2010-2025. Ann Fam Med. 2012; 10: 503-509. doi: 10.1370/afm.1431
Sargen M, Hooker RS, Cooper, RA. Gaps in the supply of phy-sicians, advance practice nurses and physician assistants. J Am Coll Surg. 2011; 212(6): 991-999. doi: 10.1016/j.jamcollsurg.2011.03.005
Institute of Medicine (US), National Cancer Policy Forum. Supply and demand in the health care workforce. In: Ensuring Qual-ity Cancer Care through the Oncology Workforce: Sustaining Care in the 21st Century: Workshop Summary. Washington (DC), USA: National Academies Press (US); 2009.
Liu JX, Goryakin Y, Maeda A, Bruckner T, Scheffler R. Global health workforce labor market projections for 2030. Human Re-sources for Health. 2017.
Global Burden of Disease Study 2013 Collaborators. Global regional and national incidence, prevalence, and years lived with disability for 301 acute and chronic disease and injuies and 188 countries, 1990-2013: A systematic analysis for the Global Bur-den of Disease Study 2013. Lancet. 2015; 386(9995): 743-800. doi: Case Series | Volume 4 | Number 1 | 10.1016/S0140-6736(15)60692-4
Lewkowicz D, Wohlbrandt A, Boettinger E. Economic im-pact of clinical decision support interventions based on electron-ic health records. BMC Health Services Research. 2020; 20: 871. doi: 10.1186/s12913-020-05688-3
Patients Like Me. Web site. https://www.patientslikeme.com/ treatments. Accessed November 17, 2020.
Anthony B. Use of telemedicine and virtual care for remote treatment in response to COVID-19 pandemic. J Med Systems. 2020; 44: 132. doi. 10.1007/s10916-020-01596-5
De Grood A, Raissi A, Kwon Y, Santana MJ. Adoption of e-health technology by physicians: A scoping review. J Multidiscip Healthc. 2016; 9: 335-344. doi: 10.2147/JMDH.S103881
Schwartz SM, Wildenhaus K, Bucher A, Byrd B. Digital twins and the emerging science of self: Implications for digital health experience design and “small” data. Front Comput Sci. 2020; 2: 31. doi: 10.3389/fcomp.2020.00031
Sexias, A, Conners, C, Chung, A, Donley T, Jean-Louis G. A pantheoretical framework to optimize adherence to healthy life-style behaviors and medication adherence: The use of personal-ized approaches to overcome barriers and optimize facilitators to achieve adherence. JMIR Mhealth Uhealth. 2020; 8(6): e16429. doi: 10.2196/16429
Alliance of Connected Care. What is Connected Care? 2014. Web site. http://www.connectwithcare.org/what-is-connected-care/. Accessed November 17, 2020.
HIMMS. 2019. Web site. https://www.himss.org/mhealth/. Accessed November 17, 2020.
Versluis, A, Verkuil B, Spinhoven P, van der Ploeg MM, Bro-sschot JS. Changing mental health and positive psychological well-being using ecological momentary interventions: A systematic review. J Med Internet Res. 2016; 18(6): e152. doi: 10.2196/jmir.5642
Schwartz SM, Ireland C, Strecher V, Nakao D, Wang C. The economic value and risk modification of a real world wellness and disease prevention program. Popul Health Manag. 2010; 13: 309-317. doi: 10.1089/pop.2009.0070
Nevedal D, Wang C, Oberleitner L, Schwartz SM, Williams AM. Effects of an individually tailored, web-based chronic pain management program on pain severity, psychological health, and functioning. Journal of Medical Internet Research. 2013; 15(9): e201. doi: 102196/jmir.2296
Schwartz S, Day B, Wildenhaus K, Silberman A, Wang C, Sil-berman J. The impact of an online disease management program Submit your article to this journal | https://openventio.org/submit-manuscript/ Case Series | Volume 4 | Number 1 | Schwartz SM et al on medical costs among health plan members. Am J Health Promot. 2010; 25: 126-133. doi: 10.4278/ajhp.091201-QUAL-377
Almario, CV. The effects of digital health technology on patient care and research. Gastroenterol Hepatol (N Y). 2017; 13(7): 437-439.
National Institutes of Health (NIH), National Institute on Aging. Aging in Place: Growing Older at Home. 2017. Web site. https://www.nia.nih.gov/health/aging-place-growing-older-home/. Accessed November 17, 2020.
Dallery J, Cassidy RN, Raiff BR. Single-case experimental de-signs to evaluate novel technology-based health interventions. J Med Internet Res. 2013; 15(2): e22. doi: 10.2196/jmir.2227
Kazdin A. Drawing valid inferences from case studies. J Consult Clin Psychol. 1981; 49(2): 183-192. doi: 10.1037//0022- 006x.49.2.183
Agency for Healthcare Research and Quality. Kravitz RL, Duan N, eds. Design and Implementation of N-of-1 Trials: A User’s Guide. Rockville, MD, USA: AHRQ Publication; 2014.
Lillie EO, Patay B, Diamant J, Issell B, Topol EJ, Schork NJ. The n-of-1 clinical trial: The ultimate strategy for individualizing medicine? Per Med. 2011; 8(2): 161-173. doi: 10.2217/pme.11.7
Mirza RD, Punja S, Vohra S, Guyatt G. The history and devel-opment of N-of-1 trials. J Royal Soc of Med. 2017; 110(8): 330-340. doi: 10.1177/0141076817721131
Nikles CJ, Clavarino AM, Del Mar CB. Using n-of-1 trials as a clinical tool to improve prescribing. Br J Gen Pract. 2005; 55: 175- 180.
Porcino, AJ, Punja, S, Chan, A-W, Kravitz R, Orkin A, Ravaud P, et al. Protocol for a systematic review of N-of-1 trial proto-col guidelines and protocol reporting guidelines. Systematic Reviews. 2017; 6: 132. doi: 10.1186/s13643-017-0525-4
Schork NJ. Time for one-person trials. Nature. 2015; 520: 609- 611. doi: 10.1038/520609a
Sniehotta FF, Presseau J, Hobbs N, Aradujo-Soares V. Test-ing self-regulation interventions to increase walking using factorial randomized N-of-1 trials. Health Psychol. 2012; 31(6): doi: 10.1037/ a0027337
Guyatt G, Keller JL, Jaeschke R, Rosenbloom D, Adachi JD, Newhouse MT. The N of 1 randomized controlled trial: Clinical usefulness. Ann Intern Med. 1990; 112: 293-299. doi: 10.7326/0003- 4819-112-4-293
Fitbit inspire HR. 2019. Web site. https://www.fitbit.com/in-spire/. Accessed November 17, 2020.
Downloads
Published
How to Cite
Issue
Section
License

This work is licensed under a Creative Commons Attribution 4.0 International License.
All content published in Clinical Trials and Practice – Open Journal (CTP) is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This license allows anyone to copy, distribute, transmit, and adapt the work — including for commercial purposes — provided appropriate credit is given to the original author(s) and source.