Big Data Analytics Paves Way for a New Era in Healthcare

Medical professionals, hospitals and related healthcare organizations are facing challenges to reduce costs, provide coordinated patient care, standardize healthcare quality and deliver effective patient outcomes. Standard medical practice is moving from relatively ad-hoc and subjective decision making to evidence-based healthcare. A McKinsey study identifies a set of converging trends in the healthcare industry to a tipping point where Big Data and Analytics will play a major role. The trends are:

  • Demand for better data owing to cost-reduction pressures
  • Availability of relevant data at scale that includes
  • Clinical data in the form of EMRs and information exchanges
  • Non-healthcare consumer data
  • Technical capability
  • Government enabling and catalyzing market change

As US healthcare providers have dramatically increased the usage of Electronic Health Record system, according to data shared by US Department of Health and Human Services, “More than half of eligible professionals and 80 percent of eligible hospitals have adopted these systems, which are critical to modernizing our health care system.” This digitization drive is leading to generation of large volumes of data. Additional sources adding on to healthcare data include:

  • Development of new technologies such as capturing devices, sensors, and mobile applications
  • Extraction of genomic information has become much cheaper and quicker
  • People have become very active on social media channels
  • Interactions with various healthcare organizations through digital forms are increasing
  • Enormous amounts of medical knowledge/discoveries are being accumulated

In 2012, worldwide digital healthcare data was estimated to be equal to 500 petabytes and is expected to reach 25,000 petabytes in 2020.

Building analytics competency can help healthcare organizations harness “big data” to create actionable insights, set their future vision, improve outcomes and reduce time to value.  Leading healthcare organizations use analytics to differentiate, see the future and drive revenue growth. In a series of interviews with healthcare executives conducted by IBM Institute for Business Value, health care professionals feel that three business objectives can be addressed by Analytics in the Healthcare industry:

  • Improve clinical effectiveness and patient satisfaction
  • Improve operational effectiveness
  • Improve financial and administrative performance

How are Health Care Organizations Realizing these Benefits?

Improved Quality of Patient Care: As Healthcare Quality is one of the primary concerns, Analytics can assist in maintaining high quality of patient care by analyzing health outcomes data for different services to pinpoint lags in providing effective treatments consistently across a patient population or geographic area.

GE Healthcare leverages SAS technology to analyze patient health datasets and look for specific patterns and trends that help hospitals prevent adverse medical events. The SAS software mines patient-related data and provides critical insights, best practices, and benchmarking to enable clinicians to make informed decisions aimed at reducing medical errors and improving the quality of care. GE’s Patient Safety Organization provides its members a single common medical event-reporting platform, with comprehensive data analytics and advisory support to identify the root causes of risk, and help hospitals make lasting safety improvements.

Baptist Health’s CHF reduction initiative is a successful example of combining data analysis with new approaches to care delivery to improve quality and reduce costs. Data was merged from multiple sources across Baptist Health to present a full picture of the causes of CHF (Congestive Heart Failure) re-admissions. This data was analyzed to identify at-risk patients, determine resource utilization rates, and assess progress on a set of quality benchmarks. Dashboards offered providers the tools needed to use this data at the point of care, and education affirmed new roles and responsibilities. As a result Baptist Health was able to achieve its goals and reduce re-admissions for CHF patients at relatively low costs.

Improved Revenue Cycle Management: Revenue cycle management (RCM) refers to the process of managing claims, payments, and revenue generation and relies heavily on a combination of claims data, clinical data, and analytics technology. Analytic tools can help healthcare organizations determine patient eligibility, validate coverage, authorize services, assess payment risk, manage submissions, and track performance.

In a recent SAS podcast, Graham Hughes, the chief medical officer of analytics provider SAS, talks about the difference analytics can make in examining the cost and quality of health care in the United States. Hughes discusses a new analytics program SAS has launched to collect and compare health care payer claims data. By collecting this information in a data warehouse, the software allows users, including state agencies, health care providers, researchers, and eventually consumers, to analyze and compare health care cost data within a county or state. This solution can go a long way in eradicating opaque medical bills and establish transparent pricing policy in healthcare.

Better Resource Utilization: Analytics can build effective processes which result in removing system bottlenecks and reducing wastage. By appropriately estimating patient volumes, length of stay, and/or waiting times, inventory control systems and supply chain management processes can be effectively redesigned. Real-time data interpretation helps tremendously in developing efficient and optimized workflows.

Fraud and Abuse Prevention: Fraud refers to a calculated misrepresentation of facts aimed at convincing payors to process a false claim for financial gain while Abuse refers to neglect of accepted business or medical practices resulting in higher reimbursements. Cost trending and forecasting, care utilization analysis, and actuarial and financial analysis are commonly used analytic applications for preventing such cases.

Population Health Management: Healthcare providers carry the responsibility to educate people, spread awareness about lifestyle changes leading to disease prevention as well as its treatment. Their timely action in reaching people before, during, and after they need specific medical attention can save many lives. Analytics plays a key role here by assisting healthcare organizations in recognizing populations consuming the most resources or at greatest risk for hospital readmissions, enabling them to target high-risk groups to reduce costs and improve outcomes. Real-time data insights can help identify trends in disease prevalence, compare the effectiveness of different treatment options, and derive best practices.

The Louisiana Department of Health and Hospitals, for example, recently teamed up with geographic information systems (GIS) software vendor ESRI to map epidemiological issues, such as babies with low birth weights. Using ESRI’s GIS mapping software, the LDHH plugged in 354 points of data for every live birth in Louisiana. Sophisticated algorithms identified clusters among locations and then generated a map based on this data. For example, by crunching low birth weight records with geospatial data, the LDHH discovered correlations between low birth weight rates and crime-riddled neighborhoods. By flagging neighborhoods with low birth weights, preventative healthcare measures can reduce the number of high-risk births, thereby cutting healthcare costs.

According to the CDC, 26 million Americans currently have asthma that costs $3,300 per person annually in treatment costs. Asthmapolis, one of a new generation of digital health startups, has designed snap-on, Bluetooth-enabled sensors that track how often people are using their inhalers (along with location and time-of-day), along with analytics and mobile apps to help them visualize and understand their triggers and trends while receiving personalized feedback. In turn, the data collected by the solution enables doctors to identify patients who are at risk or need more help controlling its symptoms. This allows them to potentially prevent attacks before they happen, saving them the cost of hospitalization or a trip to the emergency room. In fact, Asthmapolis’ early studies found that this access to real-time data was able to reduce the number of people with uncontrolled asthma (or those not regularly using inhalers) by 50 percent.

Despite the fact that there is a lot of data that the hospitals already have (through their EHR systems), there are important pieces of data that the hospital system does not have. e.g., they do not know if the patient is a smoker or not (unless the patient fills that in their form), the size of their households, their consumption of various luxury, health and vice-goods etc. HealthVue, a Raleigh, NC based start-up is bringing together data from consumer sources, geo-spatial data, CMS data and combine it with EHR data to deliver a comprehensive and timely view of the populations.

Big Data initiatives can transform the healthcare industry to make it more coordinated and streamlined and be readily available at the right time, saving millions of lives and making high-quality patient care the new norm.

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