The technology sector is increasingly seeing an opportunity to apply its expertise in data aggregation, predictive analytics, data mining and software to healthcare. Technology companies are continuing to move into the healthcare space, bringing new ideas and options in order to help improve the quality of life.

Healthcare leaders are required to navigate through a complex array of databases and technologies which can help them make the right decisions about the optimal tools to invest in for their organizations. They often have to make those decisions within limited time and budget while also balancing them against other priorities.

Reasons for a Data-Driven Healthcare

The rising cost of healthcare in the North Americas and Europe have created a need for big data in the healthcare sector. A McKinsey report states, “After more than 20 years of steady increases, healthcare expenses now represent 17.6 percent of GDP — nearly $600 billion more than the expected benchmark for a nation of the United States’s size and wealth.”

Clearly, we are in need of some smart, data-driven thinking in this area. This is why we need to have a data-driven approach to healthcare:

The necessity to reduce cost

Traditional cost reduction strategies are not enough to achieve and sustain the transformational goals for healthcare reform.

Healthcare leaders are now exploring Enterprise Intelligence (EI) systems that can help make a decision for the cost and quality of care across the complex care continuum by progressively providing granular volume, cost, and profitability details at every level of the care network and operations.

Government healthcare institutions like Centers for Medicare and Medicaid Services (CMS), are using data to reduce fraud, promote price transparency, and reveal price variations for clinical services.

2. Data and Analytics aid in Clinical Transformation on the Front Lines of Both Care Delivery and Research.

One of the most exciting areas of growth is to integrate data and predictive analytics from new platforms such as smartwatches and smartphones into existing clinical protocols to provide accurate clinical decision support.

Precision medicine is a newly emerging area that has already provided a significant number of real-world cases to prove its value. Life sciences companies, providers, and clinical researchers are ensuring an increase in new genomic, biomarker, and molecular data to appropriate accurate diagnoses and more targeted treatments.

3. A new value-based approach by different sectors.

Providers: Many leading health systems are building data and analytic capabilities to monitor and manage performance, reduce adverse events and re-admissions, and improve quality.

Public sector: Centers for Medicare & Medicaid Services (CMS) has implemented a range of alternative payment models and this has become a driver behind both sharing and using data to help providers focusing on achieving quality and performance management goals.

Life sciences companies: Life sciences companies have begun to explore value-based contracting with payers. This is a relatively new approach and demonstrates that value-based thinking is extending into the life sciences sector.

Challenges in Data-Driven Healthcare

With technological innovations such as the Internet of Things providing data at unprecedented levels, the demand for experts in data analytics is increasing. With such a drastic spike in data, there are bound to be a few challenges:

Data is different from meaningful information

A Stanford University report noted that by 2020, 2.3K exabytes of healthcare data will be generated annually by the users. This is enough to fill the typical PC about 100 billion times.

Often such data is unrefined, unstructured, lacks standard definitions, and proven use cases. To ensure a data-driven health care system in the future, organizations are developing and implementing strategies that will define their data and analytical needs and connect the right data sources with the right users at the right time. Commercial vendors for data analytics are expanding their capabilities, methods and reporting tools as the market evolves to aid the emerging market requirements.

2. Restrictions on data access to clinical institutions.

Consumers, specifically the millennials and Gen Z at home with the Internet of Things (IoT), are more comfortable using their own devices to monitor their health and well-being. This helps generate vast amounts of clinical data.

This clinical data, if made public will start raising questions concerning the safety and security of individual patient records. Ensuring privacy of data records is of utmost importance.

The Fetal Diagnostic Institute of the Pacific had a breach in security on June 30 which compromised the data of 40,800 patients. Officials were quick to act. They had to contain the incident so they enlisted a cybersecurity firm.

3. Data is seen as a valuable asset.

This can cause hospitals to keep the data protected and not make them public or share such data. Hospitals recently have started to use an electronic health record system, which means there is a lot of patient data to protect and process. But there are new methods of using and sharing that data, which could end up fundamentally shifting the way the healthcare industry functions.

Earlier this year, Apple announced a partnership with 13 major healthcare systems, including Johns Hopkins and the University of Pennsylvania. This will allow Apple to download patient’s’ electronic health data onto their Apple devices (with patient permission). This could liberate healthcare data for game-changing new uses, including empowering patients as never before.

Since electronic health records (EHRs) became popular in the last decade, there has been growing frustration over the inability to make electronic data easy to access. If the patients entered the data into the health system it would be more accurate and it would be available for more sophisticated analysis in support of improved patient care and research.

Data sharing between different entities is quite complex. It carries business risk, and efforts to pool data are delayed by legal and regulatory frameworks.

Health systems were reluctant to share the existing data, it was seen as a valuable proprietary asset. The technology to give outside entities access to electronic records kept by hospitals and doctors was still underdeveloped.

Medicaid are pushing in the direction of providing both incentives and penalties to encourage data holders to share information. In order to ensure that they share their data, providers need to know that what they gain is more valuable than what they might lose in terms of perceived competitive advantage.

4. Data Gaps in organizations.

Data gaps are a mega challenge. Organizations struggle with significant data gaps in a host of areas, which includes the universe of providers, clinicians and service locations, as well as around payers and enrollment.

Organizations have abundant data but they need to analyze it in order to evaluate where data needs to be produced so that could be beneficial for its operation. This requires time and people which could add cost in the initial few stages, therefore, it would not be the most ideal choice for many organizations.

In the long run, a data-driven healthcare systems success will be determined by whether key stakeholders can demonstrate value from the data-to-workflow connections they establish.

NHCT has taken a step in this direction by providing an app that helps share data and ensure transparency. At the same time, the data is secure. It is still under the user’s discretion to share the data.