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McKinsey on Big Data in Health Care

 

Key drivers for big data:

  • Fiscal concerns
  • Moves to value-based reimbursement
  • Aggregated, live data sets provide best evidence for decision making

Key barriers to adoption:

  • patient privacy
  • reluctance to take a holistic, patient-centred approach to value

Pathway to a new value framework:

  • right living (prevention)
  • right care – correct Dx, Rx, Mx + coordination/sharing
  • right provider – workforce innovation
  • right value – outcomes-based reimbursement
  • right innovation – R&D to reduce costs, not increase it

Exemplars of Big Data in Health

  • Kaiser Permanente has fully implemented a new computer system, HealthConnect, to ensure data exchange across all medical facilities and promote the use of electronic health records. The integrated system has improved outcomes in cardiovascular disease and achieved an estimated $1 billion in savings from reduced office visits and lab tests.
  • Blue Shield of California, in partnership with NantHealth, is improving health-care delivery and patient outcomes by developing an integrated technology system that will allow doctors, hospitals, and health plans to deliver evidence-based care that is more coordinated and personalized. This will help improve performance in a number of areas, including prevention and care coordination.
  • AstraZeneca established a four-year partnership with WellPoint’s data and analytics subsidiary, HealthCore, to conduct real-world studies to determine the most effective and economical treatments for some chronic illnesses and common diseases. AstraZeneca will use HealthCore data, together with its own clinical-trial data, to guide R&D investment decisions. The company is also in talks with payors about providing coverage for drugs already on the market, again using HealthCore data as evidence.

McKinsey_BigData_Offerings

Ginger.io

Another company, Ginger.io, offers a mobile application in which patients with select conditions agree, in conjunction with their providers, to be tracked through their mobile phones and assisted with behavioral-health therapies. The app records data about calls, texts, geographic location, and even physical movements. Patients also respond to surveys delivered over their smartphones. The Ginger.io application integrates patient data with public research on behavioral health from the National Institutes of Health and other sources. The insights obtained can be revealing—for instance, a lack of movement or other activity could signal that a patient feels physically unwell, and irregular sleep patterns (revealed through late-night calls or texts) may signal that an anxiety attack is imminent.

Key Assumptions

  • Value-based payment reform must continue
  • There will be a willingness to progress, innovate and learn from other sectors
  • Privacy issues prevail

 

Notes from interview with Nicolaus Henke (video)

  • data availability
  • easier and cheaper to link data sets and then compute them
  • understanding population health better – predict who’s going to get sick, especially with regard to chronic disease – better clinical and economic outcomes

Current opportunities for providers:

  • understanding, predicting and preventing diseases in individuals and populations
  • linking up the health system around the patient
  • understanding value (holy grail) – where are funds being directed, how can they be moved around to optimise outcomes and made more efficient

Future opportunities – change the practice of medicine altogether:

  • Medicine is currently an art that involves the application of heuristic judgement by highly trained professionals distributed around the world
  • Imagine a future where half of all diseases are well characterised, and can be automatically detected sensors embedded in our environment

Building capabilities

  • We currently mainly capture clinical and payment transactional data
  • How do we capture and exploit new, less structured data – behavioural, genomic, environmental – allows prediction
  • Managing very large data sets – totally new skill set
  • Analytics
  • Understanding the consumer better (a la other industries)
  • Health economics and value analysis – where can we invest on the margins to save money
  • Clinical leadership is critical – they need to be inspired and engaged in order to create new models of care and improve their own outcomes and systems

 

PDF: The_big_data_revolution_in_healthcare
Source: http://www.mckinsey.com/insights/health_systems_and_services/the_big-data_revolution_in_us_health_care