Tag: healthcare data analytics

The Opportunities and Challenges of Data Analytics In Healthcare

By Anita Fernandes.

Data analytics is the next step in the evolution of healthcare as it uses data-driven findings to predict and address health issues. Healthcare data analytics can also help to keep track of inventory and access methods and treatments faster than conventional systems. Data analytics is often paired with health information exchanges (HIEs) to provide safer and more personalized care based on patients’ medical history, chronic conditions and medications. Healthcare data analytics software extracts, translates and synthesizes vast amounts of data to reduce costs, involve patients more in their own health and wellness and improve patient outcomes.

Opportunities and practical applications of data analytics in healthcare

Data analytics in healthcare relies on big data (vast quantities of digitized information) that gets consolidated and analyzed. The application of data analytics in healthcare has life-saving outcomes as it uses data of a subset or a particular individual to prevent potential epidemics, cure diseases and cut down on healthcare costs. Here are a few of the opportunities and practical applications of data analytics in healthcare.

  1. Predictive analytics for personalized treatments

Predictive data analytics is the process of using historical data in order to make predictions that are personalized to each individual. Typically, analytics tools use information from millions of patients to help doctors make data-driven decisions and improve the delivery of care. Predictive data analytics helps to identify individuals who are at an elevated risk of developing chronic conditions based on lab tests, biometric data and patient-generated health data. Physicians can provide insight on lifestyle changes, wellness activities and enhanced services that can help patients avoid long-term health problems. This is particularly useful for patients with complex medical histories and suffering from multiple conditions,

  1. Data analytics to advance telemedicine

Data analytics and telemedicine go hand in hand as it helps to empower physicians and patients and offers opportunities for remote patient monitoring and remote clinical services. Smart devices are the future of telehealth monitoring as they monitor a patient’s vitals in real-time and communicate with other devices and cloud health information systems based on data analytics to alert physicians about potential problems and provide analysis on possible interventions. Data analytics in telemedicine can help to predict acute medical events – this doctors to alter medication dosages to avert negative outcomes and prevent deterioration of patients’ conditions. Telemedicine also cuts down on costs, reduces the need for hospital visits and allows patients to live healthier and more comfortable lives.

  1. Data analytics for real-time alerting

Hospitals have started employing clinical decision support (CDS) software that analyzes medical data on the spot and provides health care experts with suggestions as they make prescriptive decisions. However, in cases where patients are unable to make frequent hospital visits, doctors recommend wearables that collect patients’ data and send it to the cloud continuously. This data is analyzed continuously so that the system can identify potential problems and send real-time alerts to physicians. Doctors can then contact patients immediately to administer medications to prevent problem escalation.

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Consumers Worldwide Will Allow Access to Personal Data for Clear Benefits, Says Infosys Study

According the results of a recent survey by Infosys, consumers worldwide overwhelmingly will share personal information to get better service from their doctor; however, they are very discerning about how they share.

For example, Americans, Europeans and Australians feel comfortable sharing data with doctors (90 percent), banks (76 percent) and retailers (70 percent); however, the research shows contrasting nuances.

Consumers won’t readily share personal medical history with doctors. The study shows consumers understand the benefits of sharing data but remain cautious of data mining (especially in Europe): 39 percent globally describe data mining as invasive while also saying it is helpful (35 percent), convenient (32 percent) and time saving (33 percent).

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HIT Thought Leader Highlight: Milton Silva-Craig, TransUnion Healthcare

HIT Thought Leader Highlight: Milton Silva-Craig

Milton Silva-Craig, president of TransUnion Healthcare, discusses his thoughts for the future of healthcare, payment reform, new patients and financial pressures in the reform era and changes he sees on the horizon.

How has the role of data analytics changed in the healthcare industry, especially in light of the ACA and reform?

It is no longer a nice tool to have at your disposal. It is a requirement. It is the foundation necessary to support the outcomes of reform. Moving forward, reimbursement will be tied directly to outcomes and performance. The only way to measure such performance will be through the use of data. It will be the insights gleamed from the data that will allow providers to be successful in managing the intersection of patient care and a healthy business.

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Healthcare Data Analytics Post Reform: What’s Next for Organizations?


Guest post by Paddy Padmanabhan is senior vice president of healthcare analytics for Symphony Analytics.

As healthcare continue to become more “democratized” and patients start taking control of their own medical and healthcare information, the emergence of new healthcare entities like ACOs and HIEs are making huge amounts of data available. As new products and care delivery models start pulling previously underinsured and uninsured members of the population into the healthcare system. Given this new healthcare landscape, recent reports have estimated the market size of healthcare analytics to be $10 billion by 2018.  At the same time, a significant shortage of healthcare technology professionals is being forecast, with clinical informatics being one of the most sought-after skills in the coming few years.

In this democratized healthcare environment, previously ignored data sources, such as demographic data and individual credit histories, are now important aspects of analyzing patient profiles. And as we go deeper into internal environments, healthcare companies will start looking at machine data to understand patient and provider behavior

As the complexity of data sources multiplies, health insurance companies are faced with new challenges to manage member engagement, making it difficult for primary care physicians to provide care based on the limited patient information and insights they have from their internal systems alone.

In my conversations with senior healthcare executives, I get a sense that they recognize the situation but are understaffed for even their most basic reporting and analytical needs. Take, for example, the ACO marketplace. Meeting the needs of compliance reporting on thirty-three core quality measures alone requires these entities to invest in and establish a reporting infrastructure, in addition to all the other management information and dashboards they need to manage their businesses successfully and qualify for the shared savings.

Yet, traditionally, healthcare has focused on the “volume” end of analytics, namely data management and governance, and some degree of descriptive analytics. Unlike other markets, such as retail or banking, very little is happening in the area of advanced analytics and predictive modeling.

But there is a new way of approaching the relationships between the “3 P’s” (patient, provider, payer). Traditional parts of healthcare, namely the payers and the providers, have been used to doing business on a fee-for-service model for many years, and all of their information systems are set up to operate in this paradigm. The nature of the relationship was largely adversarial, with the focus being claims and payments, and a constant analysis of care delivery utilization for the purpose of contract negotiations between provider and payer. The new thinking now focuses on the patient as well — a collaborative relationship for improved outcomes and lower costs, and a solid analytical foundation becomes essential to track and manage clinical and financial outcomes.

Take the case of penalties on preventable readmissions. Many hospitals across the nation have been penalized up to 1 percent of their Medicare reimbursements for failing to comply with readmissions thresholds. Hospitals are scrambling to understand the root causes of readmissions, and prevent or minimize these from occurring. Hospital executives are concerned not just with the bottom line impact but the reputational damage that accompanies being on a list of offenders. Payers, on the other hand, are looking at their member populations and their provider networks at a macro level to identify patterns that will help them address the readmissions problem at a cohort level that goes beyond clinical analysis at an individual patient level. New tools and risk-scoring models are required to tackle this problem effectively.

The healthcare system has developed fairly mature analytical capabilities in traditional areas, such as claims and actuarial analysis in the traditional employer-based health insurance model. However they are in the very early stages of understanding how to work in a marketplace that is shifting toward individual members. Internal data alone will no longer cut it, and the risk-management models of employer-based insurance will no longer suffice.

Providers have spent huge sums of money implementing EHR systems and demonstrating meaningful use, to qualify for incentives. Yet the million-dollar questions remains:  what to do with the data? Clinical analytics and informatics has never been a focus in the fee-for-service model, so a major change of mindset is required.

There’s no question that there is a huge need for analytics, and the capacity and capabilities required to meet those needs do not exist today within the healthcare system.  There also are just not enough data scientists out there to go around, and it cannot be addressed by throwing the next new piece of technology that comes along at the problem.

The solution lies in prioritizing the areas of focus, developing a multi-year roadmap, and determining which areas are core to the business and which ones can be delivered using a combination of technology and consulting support. It’s also worthwhile considering global talent, especially from places like India where there is strong talent with backgrounds in science and applied math to take on at least some of the “heavy-lifting” aspects of an analytics program so that scarce and valuable internal resources can be focused on the domain-intensive aspects of analytical work.

It’s time for the healthcare sector to make bold, disruptive moves and embrace analytics whole-heartedly as a strategic tool for growth and profitability.

Paddy Padmanabhan is senior vice president of healthcare analytics for Symphony Analytics ( www.symphony-analytics.com), a division of Symphony Teleca.  He can be reached at paddy.padmanabhan@symphonyteleca.com.