Vison of health care in the digital age
Health care systems around the world are transitioning to new business models in order to improve patient care and operating efficiencies. The pressures on providers to find new models are significant, particularly for patients with chronic diseases— such as diabetes and cardiovascular disease—which now account for 60 percent of deaths globally.
New drugs, devices, and personalized therapies, along with innovations in delivery systems, all offer new approaches for the treatment of a wide range of chronic conditions. These innovations include digital tools and technologies such as
1) Traditional electronic health records (EHRs)
2) Data- based integrated diagnostics platforms
3) Cloud-based patient monitoring systems
4) Wearable sensing mobile devices
Health systems are applying more powerful analytic technologies—including artificial intelligence approaches such as machine-learning algorithms. And researchers are not just unlocking medical data—they are also “re-envisioning” how such data should be collected and applied.
Across health care today, data-driven analytics is a deep foundation for measuring and improving outcomes, minimizing variations in care, and demonstrating its value. To reach these benefits, health systems should seek to align stakeholders on value, bring predictive and prescriptive analytics to personalized care, and further engage and educate patients in their own care.
Done properly, efforts that integrate evidence-based data and sophisticated predictive analytics can identify patients for targeted interventions and improved health behaviors, and allocate resources more efficiently and effectively. The results will include a more effective health system, lower costs, and, most importantly, improved patient health.
CBMG may collaborate with YDY in three areas:
1. Develop infrastructure to incorporate electronic health record (EHR) specific for cell therapy and gene therapy.
2. Query the incidence of the cancers of CBMG interest from the EHRs, with additional clinical and demographic variables (e.g., gender, age, race, zip code of the home address, medication/clinical intervention, cancer stage, primary cancer location, etc), to facilitate patient recruitment in CBMG clinical trials.
3. Work with YNY using Cloud-based patient monitoring systems and remote device to monitor patients and improve care and reduce cost.
4. (Research purpose and also a case study to test infrastructure to be developed.) Incorporate environment variables with EHR. For example, season, weather, temperature, air pollution, pollen count, longitude, latitude, altitude, water quality, etc. We may apply Machine Learning approaches to study:
a. Factors associated with the epidemic diseases.
b. Disease incidence rates are increased by smog.
5. Collaboration with the Million Genome Project (MGP)
UK has completed the BioBank project, a large prospective cohort study of ~500,000 individuals. Many other countries are or going to carry on large genome projects in the similar scale. The MGP is an important project to improve health of Chinese people, which should be launched by Chinese government in the near future. It certainly is an intriguing opportunity for CBMG to get involved in the MGP. CBMG may 1) gain early access to the genetic/genomic data; and 2) establish partnership with other stakeholders in the areas of CBMG’s interest such as hematologic cancer, lung cancer and HCC. This project will need extensive discussion on the top line deliverables and many details that are key to successes.
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