The Case and Need for a Digital Health Ecosystem
Posted 6th December 2017 by Jane Williams
The current healthcare climate is on the brink of a long overdue makeover. The clinical research industry has been increasingly plagued by a resistance to change and is subsequently feeling the pain of this reluctance to modify its approach. Indicative of this trend are the rising prices of drug and medical device development, declining patient retention and adherence in clinical research, mounting administrative burdens, and exponentially growing expenses for clinical research that are unsustainable.
On average, development and approval of a new drug takes 15 years and $2.5 Billion and the medication that gets that far is the rare medication of thousands. To make matters worse, each day that clinical trial milestones are missed equates to roughly $1 Million USD lost due to lost productivity. An estimated 30% of clinical trial patients drop out while 60% are non-adherent, leading to aggravating delays and costs. To further compound this issue, 50% of clinical research sites attempt to save money by opting for paper-based trials, which ultimately results in a 30% delay and 60% increase in costs. Furthermore, clinical trial coordinators and study staff alike are burdened with the complexity of using an average of 10 different software applications, causing high turnover rates among staff, and thereby creating challenges for sponsors and CROs.
Despite major advances in access to patient data, the clinical research industry has failed to meet these with the integration, analysis, and representation of data needed to enable precise therapies and streamline the clinical trial process. With the advent of modern technological advances occurring all around us, shouldn’t our health and wellbeing also be undergoing a massive overhaul?
At present, several major forces are challenging the status quo, causing a rapid shift in the paradigm of modern healthcare. Most notably, technological advancements in genomics, transcriptomics, metabolomics, proteomics, microbiome sequencing providers, wearable and embedded sensors, and cloud-based approaches to medical and clinical research practices are promoting a new wave of healthcare commonly referred to as the digital health revolution.
At its core, the digital health narrative is marked by a patient-first approach. Patient engagement, coupled with maturing consumerism, has developed alongside the advent of wearable technology and the eagerness of consumers to adopt them. As reported by Ernst & Young, three-fifths of the global population is predicted to have at least one mobile subscription by 2021.
Due to this mobile-first shift, data capture from the consumer is based on continuous, patient-specific data in real-time. The collection of real world data allows for improved data quality that, when converged with EHRs, big data analytics, AI diagnostics, and provider services, effectively displaces treatment from the clinic directly into the patient’s daily life. Furthermore, this leads to better prognostic indications for risk of disease and can lead to earlier intervention and readier access to specialised care in order to maximise treatment outcome.
When actualised, an inter-operable, fully-integrated digital health ecosystem is comprised of a centralised individual defined by six dimensions: their “omics” data, EMR data, patient-generated data, their behaviours and motivations, their microbiome, their exposome and social determinants of health. Together, these elements interact with each other to generate an individual’s personal health cloud. This unique, dynamic digital phenotype drives intelligent insights for better understanding disease on both the patient and population level. Consequently, the signal to noise ratio for participant selection into clinical research drastically improves. Additionally, sponsors and CROs gain greater oversight of a clinical trial via remote, individualised patient monitoring, reduced investigator visits, improved communication and earlier signal detection. Ultimately, this patient-centric approach will shift the current approach to healthcare from reactive treatment to proactive prevention.
While a digital health ecosystem may sound like an idealised notion, at this very moment proofs of concept are transpiring. One specific area of biotechnology and healthcare convergence that is revolutionising the way in which we think not only about human disease, but also human health, is the microbiome. Eventually, all of us will be sequencing our own microbiome through a scalable approach. Daily collection and analysis will identify shifts in our microbial ecosystem that may be giving rise to a disease. Likewise, if we have been diagnosed with a disease, analysis of time series data will allow us to track progression of the disease and modify treatment approaches accordingly.
We will also be sequencing our whole exome, doing metabolic profiling, and getting specific recommendations for dietary recommendations, personalised exercise routines, cognitive risk assessments, sleep quality analysis, and much more. This will allow us (and our loved ones) to take health into our own hands with a healthcare practitioner’s oversight to intervene when needed.
With the advent of scientific discoveries that are generating a lot of buzz in the scientific and lay community alike, how can we move concepts from bench to bedside in a scalable way to make sure it is cost affordable and widely adopted? As with many direct-to-consumer microbiome diagnostic companies, the tests are still rather expensive for the average consumer, which keeps adoption down. Additionally, from a scientific standpoint, a one-time peek into the microbiome to see what the community looks like is an invalid approach. We need a scalable, time series approach, with longitudinal sampling for months on end to get a better understanding of the gut ecosystem and how it is contributing to disease.
There must also be a robust, holistic software solution to aggregate all sources of data into a centralised platform while leveraging AI and machine learning. This is especially pertinent in analysing biomarkers, as there are many factors that can modulate, influence, or disrupt the microbiome. Some are obvious, such as broad-spectrum antibiotics, but the average consumer may not know that the quantity of fibre they consume daily greatly influences the production of microbial end-products – short chain fatty acids -which have dramatic effects on cellular physiology.
Dr. Jack Gilbert, Director of the Microbiome Center and Chairman of the Scientific Advisory Board, for Aces Health, and John Slattery, Director of Research and Innovation for Aces Health, recently co-presented at the Microbiome R&D and Business Collaboration Forum: USA.
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