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Past Issue • Jan-Mar 2016

Big Data in Health Care: Analytics to Action

In this paper, the author discusses how big data and analytics in health care are now available at different organisational levels like providers, hospitals and pharmaceuticals and how these data can deliver insights that can be translated into actions for improving delivery, reducing cost of care and enhancing transparency.

Big data is changing the way business decisions are being made around the world. This is also true of industries such as the retail and banking sectors since big data reflects consumer preferences. Amazon and Netflix, both online distributors and creators of content, use big data and analytics in every aspect of their business. Amazon’s Holy Grail has been to predict and suggest what other products a consumer will want to purchase. They also recommend movies based on historical content, which includes a number of elements such as what users have bought in the past, which items they have in their virtual shopping carts, items they’ve rated and liked, and what other customers have viewed and purchased. Big data analytics fuels these “recommendation engines”. Big data refers to the high volume, variety and rapid accumulation of data and to analytics, which is the mining and pattern recognition within the different sources of data.

The health care industry today is under tremendous pressure to lower costs, reform policy and improve transparency. Advanced analytics and big data offer a huge opportunity to gain insight and make this transition by turning insights into action. The insights derived from this data are going to be path-breaking and the medical community should make use of it to its fullest potential. There is now an increased amount of data available electronically. Payers have almost all of their data digitised. Hospitals have in place Electronic Medical Record (EMR) systems. All the raw data that is available needs to be converted into information that can be adapted to derive insights and make decisions for improving care delivery. Leading hospital chains, with a presence in diagnostics and capabilities in data analytics and remote health care support/ tele monitoring, are poised to take advantage of this opportunity. Governments in both the US and India are throwing open their vast reserves of data and encouraging their use in numerous policies and initiatives. Big data is getting bigger. Hundreds of startups are being established to convert data into meaningful insights and helping in transmitting real-time data from interconnected supply chains. Additionally, strategic information is being gathered through unstructured digital channels: social media, smart phone applications and from the increasing stream of emerging Internet-based applications and gadgets.1 Patients are also the key stakeholders in keeping health care costs down. They are increasingly demanding more information about their health conditions.

Today, data analysis plays an important role in redefining health care delivery and how quality-based decisions are being made at various levels. Patients want more personalised, high-value information, which is more prescriptive in nature, rather than statistics and benchmarks. Big data is playing a growing role in facilitating this development and helping patients make more informed decisions. Analytics in the hands of the medical practitioner can help to improve the quality of care. This can be achieved by providing insights for filling in gaps in care (or alerts). This can be derived from both structured and unstructured data, or text that can be converted into structured data using some of the applications in the big data world such as Medical Ontologies and Natural Language Processing (NLP). Medical ontologies aim to provide a standard representation and vocabulary for describing and analysing data so as to derive meaningful inferences.2 Providers can get real-time and new insights into their patients’ ailments and can provide more specialised care and interventions.

Advanced analytics and big data offer a huge opportunity to gain insight and make this transition by turning insights into action. The insights derived from this data are going to be path-breaking and the medical community should make use of it to its fullest potential.

Data is essential, but the most effective approach to building models rarely starts with the data; instead, it originates with identifying the business opportunity and determining how the model can improve performance.3 With big data and analytics playing a critical role in any business today, the role of a Data Officer is becoming bigger and more critical. One of the main responsibilities of this role is to change the culture of an organisation. Many organisations see data analytics as a path to insights. That is only a part of the solution. Insight gained has to lead to action. Thus, as part of the cultural transformation, one of the main objectives for a Data Officer is to get the buy-in from the business community around how their data can or should be acted upon in positive ways.

Health care organisations need the ability to harness that data and create insights and actions from it that will help drive value for consumers and providers. There are two critical components to successfully implementing an analytics-to-action culture within an organisation. These are:

  • Prospective/ predictive models integrated with core operational systems
  • Reporting and analytics that show performance metrics and impact of the action with a constant feedback loop into the process to drive continuous improvement opportunities.

Data today is captured in more detail than ever before. For instance, instead of just recording that a member visited a hospital, there is detailed data available in the EMR system on the diagnosis at the time of admission, as well as discharge notes and additional lab results and values. In addition, there is now a proliferation of consumer sentiment data. Such data can be analysed and quantified. Table 1 provides examples of some of the business questions that can be answered by big data in the health care domain.

fig.1

table.1.

Governments in both the US and India are throwing open their vast reserves of data and encouraging their use in numerous policies and initiatives. Big data is getting bigger. Hundreds of startups are being established to convert data into meaningful insights and helping in transmitting real-time data from interconnected supply chains.

Payers and providers have started to implement solutions using big data applications. Independence Blue Cross, a health care payer in Philadelphia (PA) is using big data to drive engagement and improve outcomes. Independence has developed big databased models that can predict member concerns and complaints, sometimes weeks and months in advance. This allows them to engage these members proactively in order to address their concerns before they escalate, and ultimately, to increase their satisfaction levels. Independence also uses models that can predict the likelihood of hospitalisation (LOH) for members with chronic diseases such as CHF, chronic obstructive pulmonary disease (COPD) and diabetes. Primary health coaches use output from these big data-based models to coordinate care for members and their families. This new approach to member care is fostering a higher level of engagement and helps members reach their goals.

Independence has also partnered with New York University to develop models that can predict members who are likely to develop diabetes within the next two years. This will allow them to work with providers to intervene, apply appropriate clinical measures and change the course of a member’s disease. As a result of the use of big data, Independence has seen a significant increase in provider, member and patient engagement, and improved clinical outcomes.

Big data and advanced analytics offer tremendous potential to solve some of healthcare’s complex problems such as readmissions, optimising care for patients and reducing cost of care.

Big data and advanced analytics offer tremendous potential to solve some of healthcare’s complex problems such as readmissions, optimising care for patients and reducing cost of care. Today’s data-rich world offers vast new possibilities. The key to success lies in focusing on pragmatic steps that drive real value instead of chasing the latest fads.

Today’s data-rich world offers vast new possibilities. The key to success lies in focusing on pragmatic steps that drive real value instead of chasing the latest fads. In simple terms, prediction is most useful when connecting data to insights to action in a fast, repeatable way. This is what is meant by “analytics to action” or prescriptive analytics.

REFERENCES

1 LaVelle, S, E Lesser, R Shockley, M Hopkins, and N Kruschwitz (2011). “Big Data, Analytics and the Path From Insights to Value”, MIT Sloan Management Review, 52(2): 21-31.

2 Tewari, A (2014). “Medical Ontology: Big Data Big Challenges”. Available at http://www.researchgate.net/publication/260892842_Medical_Ontology_Big_Data_Big_Challenges

3 Barton, D, and D Court (2012). “Making Advanced Analytics Work for You”, Harvard Business Review, 90, 79-83.

ABOUT THE AUTHORS

  • ravi-chawla

    Ravi Chawla

    Visiting Faculty, Health Care Analytics at the Indian School of Business (ISB), and Vice President, Advanced Analytics and Client Solutions, Independence Blue Cross, Philadelphia, US. Ravi leads Independence’s cutting edge initiatives, advanced and predictive analytics and partnerships to improve the quality of patient care.
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