Main case studies: big data in healthcare
Continuous health analysis – Fitbit
Wearable devices like Fitbit or Apple Watch allow you to keep track of your activity, sleep hours, blood pressure, and more.
CareKit, ResearchKit, and HealthKit programs contain users’ data to be used by hospitals and private specialists of various profiles in their practice.
For medical workers, this is an opportunity to take control of patients’ health by constant monitoring of diseases and introducing new safe methods of treatment and prevention.
Fitbit provides a base of 90 billion hours of information about the heart rate. It is necessary for cardiologists and researchers for stroke prevention.
Fitbit also tracks over 167 billion minutes of exercise, 5.4 billion sleep nights, and 85 trillion steps.
Patients can be rewarded up to $1,500 per year for completing the tasks assigned to the device.
Digital Patient Records – HealthConnect
Kaiser Permanente is a US non-profit healthcare organization that implemented HealthConnect. The system provides a quick data exchange between healthcare providers and secure storage of records in a single digital system (EHR).
Successful results in the hospital confirmed the platform’s effectiveness: the death rate from heart attacks decreased by 24%.
The organization also launched Thriving Schools, a comprehensive assessment tool together with Healthier Generation. The purpose is to improve the physical and mental health of students in the current pandemic.
The launch of the EHR allows medical staff to review medical records, prevent allergic reactions, and analyze patient studies.
The electronic system helps generate reports, track disease outbreaks, and reduce erroneous prescriptions and retests.
Hospital clients receive their digital records with basic information. They can view all the doctor’s visits and recommendations online after logging into the server.
Accurate Human Resource Estimates – AP-HP
Turning large amounts of information into a structured database helps identify people’s needs and deliver services.
Algorithms are used to calculate the number of patients and personnel needed. As a result, the efficiency of the hospital increases, as customer satisfaction does.
For example, 4 AP-HP hospitals in Paris used big data algorithms to track patients. The system analyzed all kinds of sources over 10 years to plan the number of visitors to medical institutions on weekdays and weekends as well as at different times of the day.
The results helped to establish the number of professionals needed. Hospitals made staff changes and assigned staff according to sick visitors.
Cancer and Rare Diseases Treatment – Cancer Moonshot and UDN
Using open databases, medical researchers discover new treatment and rehabilitation methods, sharing knowledge with colleagues.
It positively affects overall productivity at the hospital level, reducing the risks of readmission and serious consequences.
For example, applying Big data analytics in healthcare research allows obtaining information about patients with cancer of the skin, stomach, liver, and more.
This approach is implemented internationally by The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC).
The Cancer Moonshot program in the United States aims to make progress in cancer treatment by creating an online network for cancer patients.
Research institutes can take advantage of information about a person’s disease from any state. The main problems were the incompatibility and confidentiality of data because local laws differ from federal ones.
A successful example is the Undiagnosed Disease Network (UDN), formed by the US National Institutes of Health together with medical facilities and institutes. They created a database of rare conditions found in several patients to investigate each case.