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Healthcare Analytics

A rapidly growing market


Alarming Facts about US Healthcare expenses

U.S. Health Care Costs are rising faster than inflation
  • U.S. Health Care Costs are rising faster than inflation.
  • Healthcare expenses crossed $3.0 trillion in 2014.
  • This is over 17.5% of U.S GDP for 2014.
  • Expenses will grow to roughly 20% of GDP over next decade.

  • The healthcare industry is betting on Analytics for rescue

Causes for growing expenses over last few years

US Health Care Expenditure distribution
  • Mainly caused by the expansion in the number of people with health insurance due to Obamacare.
  • Stronger economic growth and an older population transitioning into the Medicare system.
  • Other factors include substantial increases in prescription drug spending, fueled primarily by new high-cost specialty drugs for hepatitis C, cancer and multiple sclerosis.

Source :

Healthcare analytics and its growing demand

The Healthcare Analytics Market is expected to reach $18.7 Billion by 2020 from $5.8 Billion in 2015.

Healthcare Analytics is a combination of technology and statistical methods to generate intelligent learning models which when deployed on healthcare data (patient data, pharmacy data or claims data), can generate insights and correlations that physicians, hospitals and insurance companies can use to primarily to improve services and bring down healthcare cost. More elaborative and innovative use cases have been discussed in sections below.

Growing demand of Healthcare analytics

Rising healthcare costs, initiatives for meaningful use of incentives and adoption of EHRs combined with availability of big data in healthcare with technological advancements is fuelling the demand of Healthcare Analytics. More and more venture capital investments are being noticed into Healthcare Analytics.

On the other hand, the operational gap between payers and providers, lack of skilled personnel, and high cost of analytics solutions may restrict market growth.

The power of Healthcare Analytics

The power of Healthcare Analytics
  1. Clinical Analytics

    • Population Health Management
    • Quality Improvement and Clinical Benchmarking
    • Comparative Analytics/ Comparative Effectiveness
    • Clinical Decision Support
    • Precision Health Analytics
    • Reporting and Compliance
  2. Financial Analytics

    • Revenue Cycle Management
    • Claims Processing
    • Integrity and Fraud, Waste and Abuse
    • Risk Adjustment and Risk Assessment
  3. Operational and Administrative Analytics

    • Supply Chain Analytics
    • Workforce Analytics
    • Strategic Analytics

- Deploying Analytics for its customers

TechFerry is an analytics and IT innovation company. We have an equipped fleet of data scientists and machine learning experts who can judiciously deploy predictive analytics on healthcare data to generate surprising correlations between the data from various perspectives.

TechFerry can help healthcare companies answer difficult questions like:

  • What separates fast treatment responders from Partial or Non responders using analytics?
  • How can molecular diagnostics assist in finding a more suitable drug?
  • How to detect and handle insurance claims fraud using predictive analytics.
  • How to determine competitive insurance premium rates and optimize claims processes using predictive analytics.
  • How to, retain customers, improve profitability and optimize marketing campaigns using predictive analytics.
  • to use predictive analytics to predict the effectiveness of new procedures, medical tests and medications.

In addition, we are helping clients with healthcare analytics, which includes:

  • Manage and analyze data across multiple EHRs, Billing Systems, Care Settings.
  • Generate Risk modeling for predictive insights using analytics.
  • Using analytics to identify which patients are more at risk of degrading health.
  • which interventions make the most medical and financial sense, with the help of analytics.

Innovative use cases for deploying analytics in healthcare

Using analytics to determine competitive rates for Insurance products: Insurance companies can run predictive analytics on the insured patients health data and claims data to predict future costs for next policy renewals or use the trends for new business offers, and at the same time may want to offer Insurance plans on competitive rates.

Using analytics to increase the accuracy of diagnoses: Physicians can use predictive algorithms to help them make more accurate diagnoses. For example, when patients come to the ER with chest pain, it is often difficult to know whether the patient should be hospitalized. If the doctors were able to answers questions about the patient and his condition into a system with a tested and accurate predictive algorithm that would assess the likelihood that the patient could be sent home safely, then their own clinical judgments would be aided. The prediction would not replace their judgments but rather would assist.

Analyzing Electronic Health Records (EHR) - Using analytics we can aggregate and analyze patient's Electronic Health Records (EHR) from hospitals and other healthcare providers and make them available online at blazing speed. Analytics can bring down the cost of providing healthcare by sharing patient information to doctors and providers so as to reduce ordering duplicate tests and cut down on time taken to provide patient care. Current solutions have only a few months of historical patient information available online, plus the search is very slow

Big Data in Hospital Network - Instead of taking readings every few hours, a provider can record data from all the medical instruments in a pediatrics ward. By deploying analytics on the captured data we can look at it from different points of view. This can help physicians spot infection trends 12 to 24 hours in advance by running predictive analytics based on data from previous trends and the patient in hand. This can help start a course of treatment earlier, save the lives or shorten stays. "Predictive analytics allow researchers to develop prediction models that do not require thousands of cases and that can become more accurate over time."

Reducing preventable hospital readmissions using analytics - Hospital can reduce high 30-day readmission rates, using predictive analytics to keep patients at home. Real-time EHR data analytics can help a hospital cut readmissions based on data elements included in the patient's chart. This kind of analytics helps hospital direct more resources towards the patients with the highest risks who needed the most care, so they will do well when they leave the hospital.

Using analytics for preventive healthcare to cut down patient/provider expenses - Healthcare predictive analytics enables providers with tools they need, to proactively act on their patients' needs. By using patients' past behavior, analytics can help predict future events, such as a diabetic ending up in the emergency room since he did not refill his medication or a child with asthma requiring a hospital admission due to environmental triggers of her disease.

Hospital quality and patient safety in the ICU - The ICU is another area where predictive analytics is becoming crucial for patient safety and quality care. The most vulnerable patients are prone to sudden downturns due to infection, sepsis, and other crisis events which are often difficult for busy staff to predict. Integrating bedside medical device data into sensitive analytics algorithms can detect vital signs hours before humans have a clue.

Determining patients at high risk using analytics - EHR data fed to a predictive analytics algorithm can give clinicians an early warning about sepsis, which has a 40 percent mortality rate and is difficult to detect until it's too late. Running analytics on EHRs can provide us information, as to when aggressive diagnosis and treatment are needed and when they can be avoided.

About TechFerry

TechFerry is an analytics company specializing in Growth Analytics, Healthcare Analytics and IT Innovation.

If you want more information or if you are interested in any of our growth case studies or healthcare analytics case studies, email us at now. You can also leave us a reply at the bottom of this page.