The personalized medicine challenge: shifting to population health through real-world data

Personalized medicine (PM) is an initiative aimed at optimizing a person’s health through targeted, precise care. The field of precision health has rapidly blossomed, fed by the fertile, data-rich healthcare environment and the hype surrounding artificial intelligence (AI) and big data analytics (BDA). The ability to sequence and analyze large amounts of omics data (e.g., genomics, proteomics), enhanced by AI algorithms, has encouraged the growth of targeted therapies (Jameson and Longo 2015). Enormous databases are now fed by real-world data (RWD), automatically generated healthcare data based on records of routine medical encounters. These databases inform analyses from drug discovery to disease relapse prediction (Mc Cord et al. 2018).

The full editorial can be downloaded below.  

Artificial intelligence in medicine raises legal and ethical concerns

The use of artificial intelligence in medicine is generating great excitement and hope for treatment advances.

AI generally refers to computers’ ability to mimic human intelligence and to learn. For example, by using machine learning, scientists are working to develop algorithms that will help them make decisions about cancer treatment. They hope that computers will be able to analyze radiological images and discern which cancerous tumors will respond well to chemotherapy and which will not.

But AI in medicine also raises significant legal and ethical challenges. Several of these are concerns about privacy, discrimination, psychological harm and the physician-patient relationship. In a forthcoming article, I argue that policymakers should establish a number of safeguards around AI, much as they did when genetic testing became commonplace.

The full article from The Conversation can be viewed at this link.  

Personalized Medicine: The Trend That's Sweeping Health Care

From cloud-based medical imaging platforms to artificial intelligence-powered diagnostics, health care continues to be the epicenter of digital innovations that are geared toward boosting patient care. Personalized medicine is another new wave sweeping health care with the dual objectives of achieving more meaningful patient-to-doctor relations and lowering costs. The lynchpin of this concept is leveraging all clinical, genetic and environmental information of the patient to understand and treat diseases in a more holistic manner.

The full Forbes article can be viewed at this link.  

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.

Presentation slides from 8.27.19 eHI Data Analytics Task Force presented by Patrick Dunn, PhD, MS, MBA, FAHA, Senior Program Manager, Connected Health Center for Health Technology & Innovation American Heart Association.

Presentation outlines:

• American Heart Association (AHA) strategy
• 2020 impact goal
• Role of innovation
• Health and tech topics and interest areas
• AHA inside
• AHA's CarePlan Engine
• Issues related to the use of wearables and devices

Presentation from 8.27.19 Data Analytics Task Force prepared by Task Force Leader, Al Kinel, President of Strategic Interests, LLC.

Presentation outlines:

• Framework for non-traditional healthcare data
• Definitions of devices and wearables, PGHD, PROs, PROMs, & PREMs, and Remote Patient Monitoring
• Application of devices and wearables
• Value of Devices and Wearables
• Trends in the area
• Links to more research in the topic

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.  

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.  

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.