Use of Big data Analytics in the Healthcare Industry

Marutish Varanasi

Big Data Market Consultant

Executive summary:

Big-data initiatives have the potential to transform health care value chain and help patients achieve

better outcomes. But the fruits of these initiatives will be available only to companies who are

committed to looking beyond the current day to day work and are eager to commit resources and build

their capabilities. Only companies committed to this new line of thinking and are focusing on innovation

will be the first to reap the rewards of big data.

Healthcare industry Transforming

The healthcare industry is undergoing a significant change and transformation in view of the changes due to the penetration of IT and use of data in the society at large. This change is resulting in a host of new challenges that can change customer experiences, impact care quality and also the bottom line of the healthcare providers. With increasing demands from consumers for enhanced healthcare quality and increased value, healthcare providers and payers are under pressure to deliver better outcomes. Further the cost dynamics of healthcare are changing, driven by people living longer, the pervasiveness of chronic illnesses and infectious diseases, and defensive medicine practices. Analytics provide the mechanism to sort through this torrent of complexity and data, and help healthcare organizations deliver on these demands.

Healthcare Delivery is getting Complex

The global healthcare industry is experiencing fundamental transformation as it moves from a volume-based business to a value-based business. New market entrants and new approaches to healthcare delivery are increasing complexity and competition. But with the availability of big data analytics, the service providers can move to data-driven healthcare that generates insights to the organization which significantly improves the operational efficiency, quality of care, and profitability. Market Forces Driving Health Care Transformation

Increasing competition for the healthcare consumer

Health monitors and gene sequencing offering new opportunities

New engagement models

Increased regulatory requirements and reduced reimbursements

Customers are empowered by information, mobility and choice Why analytics now? Evidence continues to mount that healthcare is increasingly challenged by entrenched inefficiencies. These inefficiencies can be attributed to several factors, including the ineffective gathering, sharing and use of information. The increasing regulatory presence of government places additional focus on

accountability, governance and oversight on the industry. Market dynamics and competitive pressures require enhanced understanding of underlying trends and a path to differentiation. Top performers in the healthcare industry use analytics to differentiate, see the future and drive revenue growth. Beyond the general application of analytics to business goals, the development and use of analytics among top performers is geared towards achieving specific objectives and priorities. Analytics can be focused in a variety of ways to improve clinical quality of care, reduce costs and increase efficiency, and increase revenue and return on investment (ROI.

Objectives of implementing Big Data Analytics?

Big data analytics is the key to unlocking the insights from the data structured and unstructured. It empowers the healthcare providers to combine, integrate and analyze all data at once regardless of source, type, size, or format to generate the insights needed to:

Improve Patient Experiences and Outcomes

New regulations and technologies are giving patients more control over their own care, as well as the freedom to choose innovative providers who can deliver consistent, high-quality, personalized service. To compete and win, healthcare providers need the ability to monitor customer experiences in real time, minimize medical errors, and empower people working in the organizations to make the most of each customer interaction at every touch point.

Analyze structured and unstructured data together at once and quickly

The insights of analyzing structured and unstructured data together at once and quickly can provide help practitioners provide an exceptional healthcare experience because they can monitor, analyze and improve their services in totally new ways. At the same time, big data analytics can help improve patient care quality and safety, by giving healthcare providers immediate access to consolidated diagnostic information and reducing opportunities for medical errors.

Increase Operational Efficiency for Higher Profitability

With Big Data Analytics companies can identify hidden opportunities to reduce operational costs and gain efficiencies that directly impact the quality of patient experiences and the bottom line. Taking action to improve processes like these can tangibly enhance patient experiences and boost the organizations reputation in the marketplace all while reducing the cost of providing services.

Benefits and uses of Big Data Analytics?

With Big data healthcare providers get deeper insights about patients which help improve health outcomes in the following specific areas

Population Management : understand patients at risk across the population groups

Intervention : Getting into real time patient intervention using data

Health Economics : evidence that demonstrates a lower total cost of care and better

Outcomes

Patient Engagement : Managing customer feedback and reviews

Care Management : Reduce readmission rates with customized care

Customer management : Understand customer or patient needs in real time using Big data analytics and social media

The following healthcare providers use big data analytics to improve the patient outcomes across

various segments of the healthcare market. We look at few cases, were companies are effectively using

Big data analytics and technology to provide real value to customers.

Specific Use cases

3.1 Solving the big data problem at ICU and neonatal units of Children Hospitals

Challenges for Hospitals Hospitals' intensive care units (ICUs) have bedside monitors that continuously collect data streams on patient vitals such as respiration, heart rate, and blood pressure. The amount of information is vast, leading healthcare facilities to take hourly snapshots and then discard the data after several days. What the neonatal or ICU unit of Children Hospital needs to undertake If the neonatal or ICU unit of Children Hospital at any of its centers continuously collect's patients data(neo-natal or infants), there will be a benefit to the hospital from both the short term & long term data storage and analysis. Solving a specific problem For instance, if the neonatal or ICU unit of Children Hospital wants to know how environmental factors, such as ambient light and noise, impact the quality of care and patient outcomes of infants in the neonatal ICU; as these patients are perhaps the most sensitive to factors of their environment, but least capable of communicating what theyre experiencing. Big Data Platform - tomorrow's competitive advantage For neonatal or ICU unit of Children Hospitals these could be a first step towards getting on to a big data platform to better understand its patients, their conditions, and the quality of care they receive in support of its mission: to make kids better today and healthier tomorrow 3.2 Using predictive analytics for epileptic seizures Significant amount of research is being done by global majors to harness the power of analytics to help healthcare providers deliver more highly personalized care to people living with epilepsy. The goals of various companies is to deliver an interactive system that translates massive amounts of patient data and scientific literature into insights that healthcare providers can consult at the point of care to inform their treatment decisions. The identification of epileptic seizures significantly relies on monitoring and visual analysis of large amounts of multi-channel electroencephalographic (EEG) signals. With a goal of automating this time-consuming and subjective task, companies have developed a patient-speech seizure recognition model for multi-channel scalp EEG signals. The differentiation between seizure and non-seizure periods is shown by representing multi-channel EEG signals using a set of features from both time and frequency domains with visualization features from the data 3.3 Using big data to target preventable readmissions At Texas Health Harris Methodist Hospital Hurst-Euless-Bedford since fall 2012, more than 14,000 patients admitted to one Texas hospital have had a computer program analyze their medical records to help clinicians predict what type of care would improve their outcomes. The software used at 213-bed

Texas Health Harris Methodist Hospital Hurst-Euless-Bedford scans each patient's electronic health record within 24 hours of admission, looking at multiple data elements such as blood-pressure readings and blood-glucose levels. It takes all these pieces of data from the EHR, and it has an algorithm, and tells which patient is at higher risk for heart failure. Armed with 30-day readmission risk scores for heart failure, the hospital is better able to target intensive follow-up care to those patients who need it most. Interventions include prompt callbacks, a follow-up consultation with a cardiologist, and a talk with a social worker and the provision of educational materials. 3.4 How hospitals are using Predictive Analytics to Improve Sepsis Outcomes The problem

20,000 deaths per day worldwide

800,000+/year contract sepsis in the U.S.;

250K-300K sepsis deaths/year

$20+ billion annual cost to global healthcare providers

Mortality rate for septic shock exceeds 50%... and,

untreated, grows 7.6% per hour Early identification is critical and difficult

Evidence unwaveringly suggests that early administration of appropriate antibiotics reduces Mortality

Lack of early recognition is a major obstacle to sepsis bundle initiation Benchmarking Candidate EHR Rules EHR triggers may be proposed to aid early sepsis identification. For example, the traditional 4 SIRS criteria require vitals & labs:

Heart rate

Respiratory rate

Temperature

White blood cell count, Bands percentage Benchmark prior to deployment to estimate clinical impact

Using retrospective EHR data

Logging results from a live trial implementation Typical example of 500-bed U.S. hospital

Proposed EHR alert requires at least 2 out of 4 SIRS criteria

Benchmark to estimate alert volume and clinical workload

Results of running proposed alert on 4 months of real-time data

13142 patients receive the proposed alert

Over 100 alerts per day on average

Significant burden: alerts require clinical evaluation for infection & sepsis

Most alerts are false positives

Many hospitals end up ignoring or turning off SIRS alerts due to high workload

Though it can yield results with continuous training & feedback: Clinical Results

Alerts precede clinicians standard of care order of antibiotics by > 12 hours for > 45% of alertable sepsis patients, substantially improving upon results already achieved by conventional sepsis initiatives.

High alerting accuracy (specificity > 99%). Average1-3 alerts per clinical shift for a 500 bed hospital. Important for avoiding alarm fatigue.

Finally, with the escalating costs of healthcare for citizens across the world, the healthcare industry is addressing systemic inefficiencies by improving data sharing and collaboration and applying analytics to improve operations across the value chain, with a final objective to improve and change the patient outcomes for better.

Sources :

1. The big-data revolution in US health care Accelerating value and innovation Mckinsey & Co Jan 2013 http://www.mckinsey.com/insights/health_systems_and_services/the_big-data_revolution_in_us_health_care

2. National Children's Hospital Applies Enriched Data Analysis to Improve Pediatric Care & Outcomes

http://www.cloudera.com/content/dam/cloudera/Resources/PDF/casestudy/ChildrenHospital_Cloudera_CaseStudy.pdf

3. Using big data to target preventable readmissions - Joseph Conn | August 2, 2014 - http://www.modernhealthcare.com/article/20140802/MAGAZINE/308029981

4. Using Predictive Analytics to Improve Sepsis Outcomes - Apr 23, 2014 - UC Davis at HIMSS 2014. Case

Study: SIRS

http://himss.files.cms-plus.com/ChapterContent/nys/Presentation16.pdf

5. Modeling and Detection of Epileptic Seizures using Multi-modal Data Construction and Analysis https://www.cs.rpi.edu/research/pdf/08-02.pdf

6. Datameer Using Big Data Analytics in Healthcare

http://www.datameer.com/solutions/industries/healthcare.html

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Compliance-and-Cost.pdf

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