The Pillars of Personalized Medicine
IndividuALLytics® is a data-driven, evidence-based company and as such we believe that good, frequent and consistent measurement (i.e. time-series data), viewed through the lens of small data analytics can uniquely inform treatment decisions for patients and practitioners. This requires the thoughtful integration of four pillar key sciences:
Highest Quality Multiple Chronic Condition Evidence-Based Clinical Care Cycle
Our approach to research, science and solution development is built upon the existing evidence. However, this is just the beginning because each person’s response to treatments is as different as their life styles, circumstances and values. The IndividuALLytics® framework builds upon the concept of P4 medicine (i.e. predictive, preventive, personalized, participatory).  By using this framework coupled with the vision of small data and our proprietary N-of-1 analytics technology (IndividuALLytics Quotients or IAQplus™) allows for patient level science that informs the patient and practitioner about that patient’s unique response to treatment. This framework provides for adjustments and additions to treatment with quicker and more efficient treatment iterations based on their particular treatment response. (See Chronic Condition Specific IAHealthQuests).
This does not conflict with the traditions of the randomized clinical trial, nor is it inconsistent with a Big Data strategy or leveraging genomics. Our approach to small data is complementary and can add considerable new knowledge to treatment outcomes, patient needs and the personalization of care. When N-of-1 outcomes are aggregated, novel new response patterns emerge. It’s where population health meets precision medicine for best quality chronic condition care.
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For more information and supportive documentation see selected articles listed below:
 Hood, L, et al. Systems biology and new technologies enable predictive and preventative medicine. Science306 (5696), 640-643 (2004).
 Flores, M et al. P4 medicine: how systems medicine will transform the healthcare sector and society. Personalized Medicine, 10 (6), 5656-576 (2013).
Expanded Information for a Better Understanding of Personalized Medicine and Related Information.
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Dementia – Slowing Cognitive, Functional, and Frailty Decline
Social Relationships and Risk of Dementia
Social relationships and risk of dementia: A systematic review and meta-analysis of longitudinal cohort studies.
Digital Health Has Arrived
CDC Prevalence Multiple Chronic Conditions Adults in 2010 (2013)
Prevalence of Multiple Chronic Conditions Among US Adults: Estimates From the National Health Interview Survey, 2010
WHO Guideline Recommendations on Digital Health System Strengthening (2019)
Human health has only ever improved because of advances in technology. From the development of modern sanitation to the advent of penicillin, anesthesia, vaccines and magnetic resonance imaging, science, research and technology have always been key drivers.
Importance of Caregiver Support and Communications
Caregiver Social Support (2015)
A systemic review of social support interventions for caregivers of people with dementia: Are they doing what they promise?
Social Support and Thriving Through Relationships (2015)
Close and caring relationships are undeniably linked to health and well-being at all stages in the life span. Yet the specific pathways through which close relationships promote optimal well-being are not well understood.
Scalable Personalized Precision Medicine
Personalized Medicine Needs Personalized Measurement (2018)
The news point to what many practicing clinicians have long known and an increasing number of investigators and researchers are finding out: the efficacy of drug treatment is partially delivered by the context of the patient’s life, experience & support.
Science and Clinical Digital Health Coaching
The Art and Science of Behavioral Economics for Digital Health DesignBehavioral economics provides a toolkit of tactics to influence people’s decisions and behaviors. People who design digital health interventions dip into that toolkit can maximize their efficacy if they have a nuanced understanding of why behavioral economics works. We offer a targeted review of the psychology behind behavioral economics, coupled with a consideration of how individual user data and longitudinal user experience might affect how behavioral economics is applied in an intervention setting. We also offer examples from health behavior change and beyond to illustrate how behavioral economics have been used effectively to change behavior, and where gaps continue to exist.
Evaluation of a Diabetes Self-Management Program Claims Analysis (2018)
Evaluation of a Diabetes Self-Management Program: Claims Analysis on Comorbid Illnesses, Health Care Utilization, and Cost.
The Impact of an Online Disease Management Program – Highmark BCBS (2010)
The Impact of an Online Disease Management Program on Medical Costs Among Health Plan Members. This study evaluated the economic impact of an online disease management program within a broader population health management strategy.
Science and Clinical N-of-1
The History and Development of N-of-1 Clinical Trials (2017)
The N of 1 trial identifies whether an intervention is likely to benefit or cause unwanted effects in an individual patient. Mirza and her colleagues reviewed services to enable clinicians and patients to run N-of-1 trials.
Clinical-Trialist Rounds – Why Not Do an N-of-1 RCT? (2011)
Sackett suggests that physicians and researchers team up with statistical colleague(s) to collaborate with individual patients in using this powerful strategy for determining the best treatments for patients’ worst symptoms.
NIHMS the N-of-1 Clinical Trial – The Ultimate Strategy for Individualizing Medicine (2011)
N-of-1 or single subject clinical trials consider an individual patient as the sole unit of observation in a study investigating the efficacy or side-effect profiles of different interventions.
The Highest Cost of Poor N-of-1 Quality Chronic Condition Care
Historical NIH Paper National Health Spending 1960 to 2013 (2015)
U.S. health care expenditures have steadily increased as a share of gross domestic product (GDP) over the last half-century, increasing from 5.0 percent of GDP in 1960 to 17.4 percent in 2013.