Leveraging technology in the right ways can make or break the future of healthcare. One of the ways that healthcare will become more efficient, more affordable, and save more lives is through the use of big data.
What is Big Data?
Big data refers to a large amount of information being digitized, consolidated, standardized, analyzed and modeled. In healthcare, big data uses specific statistics from a population or an individual to research new advancements, reduce costs, and even cure or prevent the onset of diseases. In recent years, healthcare data collection has moved into the digital realm, making analysis faster and more accurate.
The rise of big data today means improvements not just for individual patients, but to the healthcare industry as a whole. Providers are making decisions based on more big data research rather than just their background and experience. With this new approach, the demand for big data in medicine is at an all-time high. That means the technology like SaaS BI tools and the companies that produce them are scrambling to meet the rising need.
How Big Data is Helping Healthcare Right Now
1. Managed Care
In value-based reimbursement, big data serves as the foundation for how a provider is measured and rewarded for ensuring the good health of a patient. Providers are graded on the quality of care they deliver, often derived from biometric data (BMI, A1c, blood pressure, etc.), as well as completion of yearly preventive and routine care for their patient population.
Many government-sponsored health plans use big data to report Healthcare Effectiveness Data and Information Set (HEDIS) and STAR (Medicare’s five-star quality rating system) measures to the state or CMS, from which they are scored and ranked. To the degree of risk associated with their arrangement, health plans reward or withhold payments to their providers based on data. (For their part, states also reward or withhold health plans’ performance payment based on big data.)
Today, many health plans have some type of risk stratification program in place. Risk stratification is based on using big data to ascribe a risk score (low to high) to a patient based on criteria set including—at a minimal—diagnoses, co-morbidity, gender, and age. The higher the score, the costlier the patient is to treat. With deep insight, health plans are able to implement targeted care management strategies designed for specific cohorts of their population. For example, many health plans know when a high-risk patient is discharged from a hospital, they are less likely to follow up with their provider or fill their script. For this reason, they will have a transition-of-care process to encourage the patient to follow up with their primary care provider and adhere to fill their script. The plan will also be able to determine their social determinants of health and coordinate such needs as transportation, home care or meals. With a successful transition of care, the plan avoided costly readmission.
2. More Accurate Treatment
Information gathered from big data gives providers more insights than they would have otherwise. Collecting data in these ways allows for better decisions, fewer cases of guessing, and better overall patient care. Mayo Clinic is an organization using big-data analytics to help identify patients with multiple conditions. These patients are most likely to benefit from home care, which vastly improves their quality of life. Big data can also identify those at increased risk of illness, giving them more control of their health with minimal medical intervention.
3. Preventing Cases Before They Occur
The Internet of Things (IoT) has resulted in devices like Fitbit and the Apple Watch to track physical movements and increase overall health, with capabilities to send that data to physicians so they can monitor progress. Partnerships between IoT and healthcare companies are further progressing this goal. Fitbit and United Healthcare have joined forces to reward patients up to $1,500 per year for meeting their exercise goals. Apple, Android, and other companies are implementing software into their devices to help people with diabetes. Apple is furthering the connection between technology and healthcare through programs like CareKit, ResearchKit, and HealthKit, which share user data with healthcare providers and researchers.
Traditional healthcare databases are often cumbersome and costly. Big data can help alleviate this problem through simpler design and more user-friendly maintenance. Hadoop clusters help to store big data by rebuilding failed nodes. This, in turn, makes technological mishaps much easier to recover from. Built from inexpensive hardware, Hadoop clusters run on direct-attached configurations instead of storage area networks. Hadoop’s ability to synthesize disparate data enables users to identify conditions missed by doctors.
4. Reducing Errors
The Network for Excellence in Health Innovation reported that prescription errors in the United States affect over 7 million people annually, causing about 7,000 deaths and costing United States healthcare organizations about $21 billion per year. MedAware, an Israeli startup, is trying to combat this disturbing trend by catching errors before they occur. Big data generated from this support tool can save money, reputations, and most importantly, lives.
About Joel Landau
Joel Landau is an American entrepreneur and healthcare expert. He is the founder of the Allure Group, which specializes in purchasing and improving nursing homes in the United States that are in danger of closing, and AlphaCare company.