Big Data Use-Cases in Healthcare – Provider, Payer and Care Management

In this part, we will discuss use cases specific to Healthcare industry. In general, Healthcare industry has been late adopter of technology compared to other industry verticals – Banking and Finance, Retail and Insurance. As per McKinsey report on Big Data June 2011, “…if US health care could use big data creatively and effectively to drive efficiency and quality, we estimate that the potential value from data in the sector could be more than $300 billion in value every year, two-thirds of which would be in the form of reducing national health care expenditures by about 8 percent…”.

Some of the key use cases for Provider industry are:

a. Reduce Medicaid Re-admissions – One of the major cost of Medicaid is readmission costs due to lack of sufficient follow ups and proactive engagement with patients. These follow-up appointments and tests are often only documented as free-text in patients’ hospital discharge summaries and notes. These unstructured data can be mined using text analytics and timely alerts can be sent, appointments can be scheduled, education materials can be dispatched. This proactive engagement can potentially reduce readmission rates by over 30%.

b. Patient Monitoring – Inpatient, Out-Patient, Emergency Visits, Intensive Care Units…

With rapid progress in technology, sensors are embedded in your weighing scales, glucose devices, wheel chairs, patient beds, XRay machines. All these large streams of data generated in real-time can provide real insights into patient health and behavior. This will improve the accuracy of information and significantly reduce the cost of healthcare providers. It will also significantly enhance patient experience at healthcare facility by providing proactive risk monitoring, improved quality of care and personalized attention. Big Data can enable CEP – complex event processing providing real-time insights to doctors and nurses in control room.

c. Preventive care for ACO

One of the key ACO goals is to provide preventive care to its members. The Disease identification and Risk Stratification will be very crucial business function. Managing real-time feeds coming in from HIE from Pharmacists, Providers and Payers will be key information to apply risk stratification and predictive modeling techniques. In the past, companies were limited to historical claims and HRA/Survey data but with HIE, the whole dynamic to data availability for health analytics has changed. Big Data tools can significantly enhance the speed of processing and data mining.

d. Provider Sentiment Analysis 

With social media growing at rapid pace, members are sharing their experience about providers through social channels – Facebook, Twitter, and other media. These experiences through comments, twitter feeds, blogs, surveys can be mined for gaining rich insights about quality of services.

e. Epidemiology

Through HIE, most of the providers, payers and pharmacists will be connected through network in few months to come. These will allow hospitals and health agencies to track disease outbreaks, patterns and trends in health issues across geography allowing determination of source and containment plans.

f. Patient care quality and program analysis

Natural with growth of data and insight into new information, comes the challenge to process these voluminous and variety of information to produce metrics and KPIs for Patient care quality and program. Big data provides the architecture, tools and techniques that will allow to process TB and Petabytes of data to provide deep health care analytics capabilities to its stakeholders.

Some of the key use cases for Payer industryare

a. Clinical Data analysis for improved predictable outcomes

Payer/Health Plans and Insurance companies can significantly reduce cost of care by reducing readmission, improved outcomes and proactive patient monitoring. There is a huge amount of existing clinical data that resides within organization and myriads of unstructured data coming at rapid space, Big data will be candidate to process these complex events and data to provide clinical insights to payer organization. Some of the areas that can be immediately addressed by Big data solutions:

  • Longitudinal analysis of care across patients and diagnoses; time sequencing
  • Cluster Analysis around influencers on treatment, physicians, therapist; patient social relationships
  • Analyze clinical notes (multi-structured data); no longer limited by dimensional sentiment of a relational database
  • Analyze click stream data and clinical outcomes; look for patterns/ trends to quality of care delivered.
  • Clinical outcomes can be integrated with financial information to understand performance

b. Claims Fraud Detection

Although no precise dollar amount can be determined, some authorities contend that insurance fraud constitutes a $100-billion-a-year problem. The United States Government Accountability Office (GAO) estimates that $1 out of every $7 spent on Medicare is lost to fraud. Some of the fraud examples are:

  • Billing for services, procedures, and/or supplies that were not provided.
  • Misrepresentation of what was provided; when it was provided; the condition or diagnosis; the charges involved; and/or the identity of the provider recipient.
  • Providing unnecessary services or ordering unnecessary tests
  • Billing separately for procedures that normally are covered by a single fee.
  • Charging more than once for the same service.
  • Upcoding: Charging for a more complex service than was performed. This usually involves billing for longer or more complex office visits
  • Miscoding: Using a code number that does not apply to the procedure.
  • Kickbacks: Receiving payment or other benefit for making a referral.

With Health Information Exchanges playing a pivotal role in real-time information sharing, Payer organization will have the power of information to proactively detect frauds using Pattern Analysis, Graph Analysis of cohort networks, social media insights.

c. Member Engagement

Like any industry, Payer organization like Health Insurance companies are battling to win member business. Companies are monitoring members, prospects behavior on their websites and social media.

d. Payer Sentiment Analysis 

Similar to Provider sentiment analysis, members are sharing their experience about insurance benefits, customer service experience through social channels – Facebook, Twitter, and other media. These experiences through comments, twitter feeds, blogs, surveys can be mined for gaining rich insights to improve quality of services.

e. Call Center Analysis

Payer organizations are capturing information from Call Center using call recording. These call records provide valuable information to

  • staffing model – by demographic preferences, hours of services
  • member feedback using voice pattern and recognition
  • member experience using metrics – Average speed to answer, abandonment rate, dropped calls, unable to reach member

Finally, few of the use cases for Care Management – Disease Management, Utilization Management and Behavioral Health Management industry are:

a. Disease Identification and Risk Stratification

Care management companies constantly collect data from various sources – claims, prior authorizations, biometrics screening, health risk assessment and survey data. Disease ID and Risk Stratification is key function that helps organization with limited resources to focus on top 5-10% of high risk population that takes 60-80% of medical cost. Processing through 10′s of years of historical information added with realtime information from various sources adds a huge complexity and processing challenges. Big data can alleviate such challenges by not only providing accessibility to  unstructured data but also providing the robustness and speed of processing.

b. Member Sentiment Analysis 

With social media growing at rapid pace, members are sharing their experience about providers through social channels – Facebook, Twitter, and other media. These experiences through comments, twitter feeds, blogs, surveys can be mined for gaining rich insights about quality of services.

c. Member care quality and program analysis

Natural with growth of data and insight into new information, comes the challenge to process these voluminous and variety of information to produce metrics and KPIs for Member care quality and program. Big data provides the architecture, tools and techniques that will allow to process TB and Petabytes of data to provide deep health care analytics capabilities to its stakeholders.

While these are just few of the generic use cases in Healthcare industry, there are a lot of unique use cases specific to your line of business, organization and department. I will reiterate again that assessing and prioritizing the business use case for Big data based on value is key to its success and will have significant impact on your organization in years to come. Think Big, start Small!

Advertisements

Using Big Data Analytics in Healthcare

The healthcare industry has been a late adopter of technology when compared to other industries such as banking, retail and insurance. As per the McKinsey report on big data from June 2011, “…if US health care could use big data creatively and effectively to drive efficiency and quality, we estimate that the potential value from data in the sector could be more than $300 billion in value every year, two-thirds of which would be in the form of reducing national health care expenditures by about 8 percent…”

To realize the true potential value of big data in healthcare, the industry will need major structural changes, including legislative reforms, incentives, privacy laws, reimbursement and incentive schemes. With recent health reform paving the way for health information exchange (HIE) across the nation, data is going to flow in at a much faster rate and with more variety with real-time information about members and patients. The HIPAA 4010 to 5010 move has already paved the way to capture more granular and specific information with procedure codes, revenue codes, drug codes and diagnosis codes.

Now, without getting too distracted with macro indicators, I would like to delve into industry domain-specific example use cases for improving health outcomes:

Reduce hospital re-admissions

One major cost in healthcare and one that is severely impacting Medicaid is hospital readmission costs due to lack of sufficient follow-up and proactive engagement with patients. These follow-up appointments and tests are often only documented as free-text in patients’ hospital discharge summaries and notes. This unstructured data can be mined using text analytics. If timely alerts were to be sent, appointments scheduled and education materials dispatched, proactive engagement could potentially reduce readmission rates by over 30 percent.

Patient Monitoring: Inpatient, out-patient, emergency visits and ICU

Everything is becoming digitized. With rapid progress in technology, sensors are embedded in weighing scales, glucose devices, wheel chairs, patient beds, X-Ray machines, etc. Digitized devices generate large streams of data in real-time that can provide insights into patient health and behavior. If this data is captured, it can be put to use to improve the accuracy of information and enable practitioners to better utilize limited provider resources. It will also significantly enhance patient experience at healthcare facility by providing proactive risk monitoring, improved quality of care and personalized attention. Big data can enable complex event processing (CEP) by providing real-time insights to doctors and nurses in the control room.

Preventive care for ACO

One of the key accountable care (ACO) goals is to provide preventive care. Disease identification and risk stratification will be very crucial to business function. Managing real-time feeds coming in from HIE, pharmacists, providers and payers will deliver key information to apply risk stratification and predictive modeling techniques. In the past, companies were limited to historical claims and HRA/survey data but with HIE, the whole dynamic to data availability for health analytics has changed. Big data tools can significantly enhance the speed of processing and data mining.

Epidemiology

Through HIE, most of the providers, payers and pharmacists will be connected through networks in the near future. These networks will facilitate the sharing of data to better enable hospitals and health agencies to track disease outbreaks, patterns and trends in health issues across a geographic region or across the world allowing determination of source and containment plans.

Patient care quality and program analysis

With exponential growth of data and the need to gain insight from information comes the challenge to process the voluminous variety of information to produce metrics and key performance indicators (KPIs) that can improve patient care quality and Medicaid programs. Big data provides the architecture, tools and techniques that will allow processing terabytes and petabytes of data to provide deep analytic capabilities to its stakeholders.

While this is the tip of the iceberg for the healthcare industry use cases for big data, there are lots of unique opportunities specific to your line of business, organization and department. Most healthcare organizations have begun their journey along a data analysis continuum. Assessing and prioritizing the initiatives for big data based on value is key to success and will have significant impact on your organization in years to come.

IBM has a team of experienced consultants and leading products and solutions that can help cross-organization teams assess data sources, develop a roadmap and strategy, and implement a flexible and scalable big data platform with clinical and advanced analytics capabilities.

You can start small, but remember to Think Big.