This position paper, written by Hilary Daniel, BS, was developed for the Health and Public Policy Committee of the American College of Physicians July 2017.

Document includes Background and Recommendations from the American College of Physicians on Improving Health Care Efficacy and Efficiency Through Increased Transparency.

The following are a collection of resources and initiatives related to the eHI Data Analytics Task Force (Non-Traditional Data in Healthcare) focusing on Wearables and Medical Device data.

• 12 Things about IoT Analytics every Technology CEO Needs to Know ​

• Fitbit Research Library​

• eHI Whitepaper: The Return on Investment of PGHD and Remote Patient Monitoring​

• eHI Whitepaper: Sharing Data with Entities Not Covered by HIPAA​

• Presentation: Uncover Beliefs About Patient-Generated Health Data​

• Deloitte Article: How the IoT and PGHD can unlock health care value​

• Law Blog: New Reimbursement for Remote Patient Monitoring and Telemedicine

• UHC - Navigate4Me​

• Pittsburgh Health Data Alliance – Predicting Readmissions​

• Cedars-Sinai Medical Center, Center for Outcomes Research and Education (CS-CORE) ​

• MGMA Article: Integrating Data from “Wearables” into EHRs​

• NPJ Digital Medicine Article: Best Practices for Analyzing Large-Scale Health Data from Wearables and Smartphone Apps​

• American Heart Association Inside - Collection of AHA content, derived from AHA Guidelines and Scientific Statements, and presented in a mobile friendly format.

Can Artificial Intelligence Cure Mental Health Conditions?

Mental illnesses have become one of the biggest medical challenges of the 21st century. According to the World Health Organization, around 450 million people globally are affected by mental illness. 

But two-thirds of people with a known mental condition, such as anxiety, depression and co-occurring disorders, fail to seek help from medical professionals. This can be due to a number of factors, including stigma and discrimination.

London-based digital health and artificial intelligence company BioBeats is on a mission to change the perception of mental health globally by using data. It’s developed a wearable device, an app and machine learning system to collect data and monitor users’ level of stress, before predicting when stress could be a cause of a more serious or physical health condition. 

The full Forbes article can be viewed at this link.  

Smart Medication Adherence Monitoring in Clinical Drug Trials: A Prerequisite for Personalised Medicine?

The current era of personalised medicine promises us medications tailored to the individual patient, minimising adverse effects, and maximising effectiveness. Yet, medication is only effective when taken as prescribed, which in the ‘real world’ turns out to be a major challenge. Indeed, WHO and OECD estimate that one-out-of-two patients with chronic diseases does not use their medication as prescribed. In Europe alone, non-adherence is estimated to annually contribute to the premature death of 200,000 patients and excess healthcare costs of €125 billion. Contrary to what is often assumed, the non-adherence problem is not exclusive to ‘real-world’ patients, but it also influences the strictly regulated setting of clinical drug registration trials. Of every hundred trial participants, four do not initiate a study drug. Each study day, 10–12% does not take their medication while still on treatment. In long-term studies, after one year, almost 40% of trial participants have stopped taking their medication. Novel digital adherence monitoring devices may offer a solution for patients who tend to forget their medication and for trial regulators to have granular data on the exact timing of medication use.

The full article can be downloaded below.  

Putting the data before the algorithm in big data addressing personalized healthcare

Technologies leveraging big data, including predictive algorithms and machine learning, are playing an increasingly important role in the delivery of healthcare. However, evidence indicates that such algorithms have the potential to worsen disparities currently intrinsic to the contemporary healthcare system, including racial biases. Blame for these deficiencies has often been placed on the algorithm—but the underlying training data bears greater responsibility for these errors, as biased outputs are inexorably produced by biased inputs. The utility, equity, and generalizability of predictive models depend on population-representative training data with robust feature sets. So while the conventional paradigm of big data is deductive in nature—clinical decision support—a future model harnesses the potential of big data for inductive reasoning. This may be conceptualized as clinical decision questioning, intended to liberate the human predictive process from preconceived lenses in data solicitation and/or interpretation. Efficacy, representativeness and generalizability are all heightened in this schema. Thus, the possible risks of biased big data arising from the inputs themselves must be acknowledged and addressed. Awareness of data deficiencies, structures for data inclusiveness, strategies for data sanitation, and mechanisms for data correction can help realize the potential of big data for a personalized medicine era. Applied deliberately, these considerations could help mitigate risks of perpetuation of health inequity amidst widespread adoption of novel applications of big data

The full article can be downloaded below.  

Bridging the Gaps in Personalized Medicine Value Assessment: A Review of the Need for Outcome Metrics across Stakeholders and Scientific Disciplines

Despite monumental advances in genomics, relatively few health care provider organizations in the United States offer personalized or precision medicine as part of the routine clinical workflow. The gaps between research and applied genomic medicine may be a result of a cultural gap across various stakeholders representing scientists, clinicians, patients, policy makers, and third party payers. Scientists are trained to assess the health care value of genomics by either quantifying population-scale effects, or through the narrow lens of clinical trials where the standard of care is compared with the predictive power of a single or handful of genetic variants. While these metrics are an essential first step in assessing and documenting the clinical utility of genomics, they are rarely followed up with other assessments of health care value that are critical to stakeholders who use different measures to define value. The limited value assessment in both the research and implementation science of precision medicine is likely due to necessary logistical constraints of these teams; engaging bioethicists, health care economists, and individual patient belief systems is incredibly daunting for geneticists and informaticians conducting research. In this narrative review, we concisely describe several definitions of value through various stakeholder viewpoints. We highlight the existing gaps that prevent clinical translation of scientific findings generally as well as more specifically using two present-day, extreme scenarios: (1) genetically guided warfarin dosing representing a handful of genetic markers and more than 10 years of basic and translational research, and (2) next-generation sequencing representing genome-dense data lacking substantial evidence for implementation. These contemporary scenarios highlight the need for various stakeholders to broadly adopt frameworks designed to define and collect multiple value measures across different disciplines to ultimately impact more universal acceptance of and reimbursement for genomic medicine.

The full article can be downloaded below.

Opportunities and Challenges in Interpreting and Sharing Personal Genomes

The 2019 “Personal Genomes: Accessing, Sharing and Interpretation” conference (Hinxton, UK, 11–12 April 2019) brought together geneticists, bioinformaticians, clinicians and ethicists to promote openness and ethical sharing of personal genome data while protecting the privacy of individuals. The talks at the conference focused on two main topic areas: (1) Technologies and Applications, with emphasis on personal genomics in the context of healthcare. The issues discussed ranged from new technologies impacting and enabling the field, to the interpretation of personal genomes and their integration with other data types. There was particular emphasis and wide discussion on the use of polygenic risk scores to inform precision medicine. (2) Ethical, Legal, and Social Implications, with emphasis on genetic privacy: How to maintain it, how much privacy is possible, and how much privacy do people want? Talks covered the full range of genomic data visibility, from open access to tight control, and diverse aspects of balancing benefits and risks, data ownership, working with individuals and with populations, and promoting citizen science. Both topic areas were illustrated and informed by reports from a wide variety of ongoing projects, which highlighted the need to diversify global databases by increasing representation of understudied populations.

The full conference report can be downloaded below.  

Implementing eScreening technology in four VA clinics: a mixed-method study

Technology-based self-assessment (TB-SA) benefits patients and providers and has shown feasibility, ease of use, efficiency, and cost savings. A promising TB-SA, the VA eScreening program, has shown promise for the efficient and effective collection of mental and physical health information. To assist adoption of eScreening by healthcare providers, we assessed technology-related as well as individual- and system-level factors that might influence the implementation of eScreening in four diverse VA clinics.

This was a mixed-method, pre-post, quasi-experimental study originally designed as a quality improvement project. The clinics were selected to represent a range of environments that could potentially benefit from TB-SA and that made use of the variety eScreening functions. Because of limited resources, the implementation strategy consisted of staff education, training, and technical support as needed. Data was collected using pre- and post-implementation interviews or focus groups of leadership and clinical staff, eScreening usage data, and post-implementation surveys. Data was gathered on: 1) usability of eScreening; 2) knowledge about and acceptability and 3) facilitators and barriers to the successful implementation of eScreening.

Overall, staff feedback about eScreening was positive. Knowledge about eScreening ranged widely between the clinics. Nearly all staff felt eScreening would fit well into their clinical setting at pre-implementation; however some felt it was a poor fit with emergent cases and older adults at post-implementation. Lack of adequate personnel support and perceived leadership support were barriers to implementation. Adequate training and technical assistance were cited as important facilitators. One clinic fully implemented eScreening, two partially implemented, and one clinic did not implement eScreening as part of normal practice after 6 months as measured by usage data and self-report. Organizational engagement survey scores were higher among clinics with full or partial implementation and low in the clinic that did not implement.

Despite some added work load for some staff and perceived lack of leadership support, eScreening was at least partially implemented in three clinics. The technology itself posed no barriers in any of the settings. An implementation strategy that accounts for increased work burden and includes accountability may help in future eScreening implementation efforts.

The full article can be downloaded below.  

Telehealth Has An Awareness Problem

Two-thirds of adults are willing to consult with a doctor via video, but only 8% have tried it, according to a new consumer survey by American Well, a major provider of telehealth services.

The poll closely tracks other studies that show consumers have been slow to adopt telehealth (also known as telemedicine), despite the fact that more health plans and self-insured employers are offering it as a benefit. “It’s troubling,“ says Roy Schoenberg, the co-founder and CEO of Boston-based American Well. “Now more people are covered, but they don’t know it’s available.”

Another disconnect: Although 23% of primary care physicians say they now offer virtual visits, only 6% of consumers report that their doctor does. The fact that doctors are increasingly using telehealth should help clear a major hurdle, since most patients prefer to stick to their doctors or see them in person. “The biggest revolution isn’t the adoption of telehealth by patients, it’s by doctors,” says Schoenberg, who has a medical degree. “The vast majority [of patients] follow the orders of physicians, there’s a lot of comfort in that.”

The full Forbes article can be viewed at this link.  

The Rise Of Direct To Consumer Precision Health

Healthcare trends are already moving us toward earlier intervention. Recent advancements in technology and science help medical practitioners deliver more precise and personalized health insights to patients. In the last 15 years, the cost of mapping a human genome has fallen from $100 million to below $1,000, allowing the use of data from patient DNA to become an increasingly viable method for providing accurate, actionable recommendations before symptoms occur. Many researchers believe that within ten years, all medical records will include DNA profiles.

Moreover, sensors have become smaller, enabling remote diagnostics and monitoring (think Apple Watch or Fitbit). Advancements in artificial intelligence are driving innovation in healthcare as well, helping companies harness increasingly available data to offer enhanced insights to patients. Viome, for instance, is a subscription-based service that sends microbiome testing kits to consumers and leverages AI algorithms to provide food recommendations based on the consumer’s microbe composition (Viome closed a $25 million Series B round in April 2019, including funding from Marc Benioff). This proactive and tech-enabled individualized approach to medicine is known as precision health.

The full Forbes article can be viewed at this link.