Volume 4, Issue 1

  • 2021, January

    case series

    The Promise of Small Data for Telemedicine in Chronic Condition Management: A Real-World Case SeriesOpen Access

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    Abstract [+]

    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.
    Telemedicine; Small data; n-of-1; Internet of things; Chronic conditions; Self-management; Predictive analytics.

  • 2021, August


    The Case for Digital Pill Use in Clinical TrialsOpen Access

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    Abstract [+]

    Medication adherence in clinical trials is significantly overestimated through every phase of drug development. This can cause a reduction in statistical power, potentially resulting in incorrect conclusions regarding efficacy, safety, tolerability, and dose-response relationships, in addition to major cost overruns. Digital pill systems enable adherence measurement through an embedded ingestible sensor paired with an external receiver. An oral pharmaceutical product is over-encapsulated by a pharmaceutical-grade shell containing a biocompatible sensor. Upon exposure to gastrointestinal fluid, the shell dissolves and the sensor is activated. Medication ingestion data is transmitted via a digital signal. Clinicians and researchers use this data to track, in real time, when and if a medication was taken. These systems have demonstrated a 99.4% rate of accuracy, and have over 15-years of supporting
    experience and safety data. Spurred by the accelerated adoption of technology in healthcare and in everyday life, patients have become tech-savvy. They quickly adapt to these devices, and are able to use them safely and effectively. Digital pills can be implemented in most types of studies. In early-stage trials such as pharmacokinetic and pharmacodynamic studies, or dose-finding studies, accurate information on maximum-tolerated dose levels is essential and cannot be established unless study participants are highly adherent. In later-stage pivotal trials, effective medication adherence tracking can strengthen the dataset and confidence in the study results. Significant nonadherence may generate results that do not meet statistical or clinical significance for the critical endpoints, resulting in at worst, a failed trial, or at best, the need to enroll additional patients at substantial additional cost. Most clinical trials fail to achieve statistical significance, and poor medication adherence is often an important contributor. A digital pill system can ensure the quality and integrity of adherence data, increase confidence in the overall study data, and improve clinical trial efficiency.

  • 2021, November

    original research

    Design and Statistical Methods for Handling Covariates Imbalance in Randomized Controlled Clinical Trials: Dilemmas ResolvedOpen Access

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    Abstract [+]

    In practice, between groups baseline imbalance following randomization not only opens effect estimate to bias in controlled trials, it also has certain ethical consequences. Both design and statistical approaches to ensure balanced treatment groups in prognostic factors are not without their drawbacks. This article identified potential limitations associated with design and statistical approaches for handling covariate imbalance in randomized controlled clinical trials (RCTs) and proffered solutions to them.
    A careful review of literatures coupled with a robust appraisal of statistical models of methods involved in a way that compared their strength and weaknesses in trial environments, was adopted.
    Stratification breaks down in small sample size trials and may not accommodate more than two stratification factors in practice. On the other hand, minimization that balances for multiple prognostic factors even in small trials is not a pure random procedure and in addition, could present with complexities in computations. Overall, either minimization or stratification factors should be included in the model for statistical adjustment. Statistically, estimate of effect by change score analysis (CSA) is susceptible to direction and magnitude of imbalance. Only analysis of covariance (ANCOVA) yields unbiased effect estimate in all trial scenarios including situations with baseline imbalance in known and unknown prognostic covariates.
    Design methods for balancing covariates between groups are not without their limitations. Both direction and size of baseline imbalance also have profound consequence on effect estimate by CSA. Only ANCOVA yields unbiased treatment effect and is recommended at all trial scenarios, whether or not between groups covariate imbalance matters.
    Randomization; Covariate imbalance; Stratification; Minimization; Change score analysis; ANCOVA.