Data is driving fee-for-value healthcare. Every firm is racing to figure out how to bring the power of data science to streamline healthcare encounters ~ member/consumer engagement, provider/PCP engagement or clinical/care engagement.
Health expenditures in the United States crossed $3.2 trillion in 2015 which is more than ten times the $256 billion spent in 1980.
Almost 15% of U.S GDP is spent on healthcare…a staggering number. As a mega-vertical, healthcare covers several major segments (the 7 Ps)
- Payers (Health Insurance and Health Plans),
- Providers (Hospital Systems, Labs and IDNs),
- Pharmacy (retail distribution networks), and
- Pharmaceutical and medical equipment manufacturers,
- Prescribers (Physicians, clinics and pharmacy minute clinics)
- Police (Regulators, FDA)
- Patients (consumers)
U.S. healthcare system is a complex beast and difficult to navigate – providers need to make it easier for patients. They are using people resources like care coordinators and patient navigators to help patients navigate the system.
The focus on the payer side is in digitizing health today is to reduce the amount of waste in the health care system via implementation of new forms of health IT and Analytics… that reduces inefficiencies, redundancies and administrative costs.
According the CEO of Aetna…”the health care system wastes more than $765 billion each year – that’s 30 percent of our health care spending.”
While spending on health care is dominating headlines, the health care industry (7Ps) is in a state of flux. Stakeholders across the health care sector are running hard to reduce costs. The drivers impacting cost of healthcare include:
- Aging population – Patient history and patterns of care impacting patient readmission rates
- Rise in Chronic Disease – 75% of cost – Prevention not reactive medicine
- Drug cost – escalating for certain therapies (Generics exchanged for biological drugs)
The healthcare ecosystem is being reshaped by two powerful counter economic forces at work: (1) Improve quality of care and (2) drive the cost of care down. Basically spend less and get more.
As a result, the entire healthcare ecosystem is changing into a “information-driven”, “evidence-based” and “outcome-driven” model.
The target healthcare transformation goals are:
- align economic incentives between payers and providers,
- digital engagement…create a simpler, more transparent consumer experience, and
- connected health….technologies that seamlessly connect our healthcare system.
In this posting we look at Digital Health Care use cases and how data and analytics are being slowly but sure being adopted in the form of informatics. All this change is being driven under the guise of Health Reform.
As a data engineer and scientist, I have been following the NSA PRISM raw intelligence mining program with great interest. The engineering complexity, breadth and scale is simply amazing compared to say credit card analytics (Fair Issac) or marketing analytics firms like Acxiom.
Some background… PRISM – “Planning Tool for Resource Integration, Synchronization, and Management” – is a top-secret data-mining “connect-the-dots” program aimed at terrorism detection and other pattern extraction authorized by federal judges working under the Foreign Intelligence Surveillance Act (FISA). PRISM allows the U.S. intelligence community to look for patterns across multiple gateways across a wide range of digital data sources.
PRISM is unstructured big data aggregation framework — audio and video chats, phone call records, photographs, e-mails, documents, financial transactions and transfers, internet searches, Facebook Posts, smartphone logs and connection logs – and relevant analytics that enable analysts to extract patterns. Save and analyze all of the digital breadcrumbs people don’t even know they are creating.
The whole NSA program raises an interesting debate about “Sed quis custodiet ipsos custodes.” (“But who will watch the watchers.”) Read more
Over the past seven years, we’ve seen a massive regulatory overhaul and an industry-wide push to enhance trust and confidence and encourage investor participation in the financial system.
To roadmap Wall Street regtech priorities, we have been having ongoing meetings with MDs and leading architects in global banks and investment services firms. RegTech (e.g., regulation as a service) is a subset of FinTech. Companies include
- Fintellix offers a data analytics platform allowing banks to convert internal data into regulatory reporting formats
- Suade offers banks “regulation as a service” interpreting real time regulatory knowledge so that banks can better manage and respond to regulation
- Sybenetix combines machine learning with behavioral science to create a compliance and performance tool for traders
No longer business as usual. It is clear that banks are devoting more resources to Know Your Customers (KYC), Anti-Money Laundering (AML), fraud detection and prevention, Office of Foreign Assets Control (OFAC) compliance. FINRA is at the beginning stages of the process for building the Consolidated Audit Trail, or CAT for trading surveillance.
To enable compliance with variety of Risk/Regulatory initiatives, AML and KYC initiatives…the big RegTech related investments are:
- Strengthening the Golden Sources – Security Master, Account Master and Customer Master.
- Standardized, common global business processes, data, systems and quantitative solutions that can be leveraged and executed across geographies, products, and markets to manage delinquency exposures, and efficiently meet Regulatory requirements for Comprehensive Capital Analysis and Review (CCAR), FDIC Reporting, Basel, and Stress Loss Testing.
- Various enterprise data management initiatives – Data Quality, Data Lineage, Data Lifecycle Management, Data Maturity and Enterprise Architecture procedures.
Regulatory reporting improvements via next generation Enterprise Datawarehouses (EDW) (using Oracle, IBM, NoSQL or Hadoop)– Reporting on top of EDW addresses the core problems faced by Finance, Risk and Compliance when these functions extract their own feeds of data from the product systems through which the business is conducted and use differing platforms of associated reference data in support of their reporting processes.
Lot of current investments are in the areas of Finance EDW which delivers common pool of contracts, positions and balances, organized on an enterprise wide basis and completed by anointed “gold” sources of reference data which ensure consistency and integration of information.
Crawl, walk, Run seems to be the execution game-plan as the data complexity is pretty horrendous. Take for instance, Citi alone….has approximately 200 million accounts and business in 160+ countries and jurisdictions. All risk management is made incredibly complex by the numerous banking mergers that took place over the past 3-4 decades.
The type of data challenges global banks like Citigroup, Goldman, Wells Fargo, Bank of America and JP MorganChase are wrestling with include: Read more