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Predictive Analytics in Health Care: Helping to Navigate Uncertainties and Change


Predictive analytics can help navigate the many coming changes in US healthcare, including aging population, less money, impact of ACA (Obamacare) - especially on pharmaceutical and medical supplies, the R&D process, investment and new product launches.



By Lana Klein (co-founder, 4i), Sept 2014

A recent billion-dollar forecasting error in Walgreen’s Medicare-related business has shocked the company and investors. The CFO was pressured to leave after he cut his 2016 pharmacy-unit earnings forecast from $8.5 billion to $7.4 billion. The company explained that it had not factored in a spike in the price of some generic drugs that it sells through its annual Medicare Part D contracts.

While an error of this size draws the attention of the press and sends shock waves through the industry, Healthcare companies are regularly buffeted by armies of similar but smaller scale issues.

Walgreen’s forecasting mistake highlighted a common problem: ever-growing business complexity, with interconnections between many market, business and regulatory drivers. Addressing this new level of complexity with traditional business methods is no longer sufficient. To remain competitive and successful, Healthcare companies need robust analytical tools that can cut through gigabytes of data, dozens of issues and find connections between many different factors.

When it comes to advanced analytics, Healthcare is still behind many other industries such as Finance and Consumer Goods. However, the industry is rapidly discovering that Predictive Analytics can help to solve some of the most critical issues it faces today:

  • As the population continues to age and ACA takes hold, we will need more people in the medical profession. How many more people will be coming under coverage? When?
  • How can we treat more people with less money?
  • How will ACA affect pharmaceutical and medical supplies companies?
  • What are the implications to the R&D process, investment and new product launches?

We will need to Treat More People…

The total demand for Healthcare services is projected to grow 14 percent by 2020. About 80 percent of this additional demand is likely to come from aging, with the remaining 20 percent from expanded coverage under ACA.

At the same time, the number of primary care doctors is projected to grow only by 8 percent, leaving a likely shortage of 20,400 primary care physicians. How can the industry close this gap?

How analytics can help:

Forecasting future demand for different types of healthcare services and pharmaceutical products is the first step. As Walgreen recently discovered, forecasting is a more complex exercise than just extending historical trends forward. Advanced forecasting methods identify and take into account a multitude of factors that are relevant to a specific service or product, such as aging, expanded coverage, incidence of specific conditions, insurance coverage levels, out of pocket payments, pricing, competing or substitute products. Some of these factors, such as aging, are highly predictable and stable. Other factors, such as insurance coverage, pricing, and lines of treatment, change dynamically. Good forecasting models will reflect the nature of these factors and adjust accordingly, giving users the ability to simulate different scenarios.

… With Less Money and Shifting Payment Models

While we will have more people to cover, health care budgets will not increase proportionately, translating into fewer dollars per patient, and putting pressure on pricing and margins throughout the entire value chain from pharma and device manufacturers to health care providers.

Health and industry value generation

The industry tax will further compound margin pressures, taking billions of dollars of revenue from branded market and device companies.

Changes to payment models will likely cascade across the industry and affect all participants, from private insurers and government which will develop new models, to providers and patients who will change their behavior accordingly.

Each of these steps can affect the eventual decision to prescribe or use a specific health care product or service, as well as how much this product will cost and how it will be paid for.

And it is clear that ACA will result in additional future price pressures, the impact of which will vary greatly by product, therapeutic area, service and state.

How analytics can help:

Predictive models can estimate the impact of these factors on specific products, services and states:

  • Supply chain analytics can help to effectively take out production and distribution costs to offset price reduction and protect margins;
  • Optimization analytics can help to make processes more efficient: locate resources (ambulances, clinics, etc.), optimize schedules of doctors and nurses, optimally route delivery vehicles, and assign spatially located patients to less busy medical facilities
  • Price analytics can help brands determine appropriate levels of pricing and promotion in changing market conditions;
  • Value chain analytics can help identify areas of price or cost reductions that will have the least impact on quality of service and goods

Doctors will have less influence

Medical decisions will be more regulated and regimented, with payees increasingly mandating what doctors can prescribe, and doing so first and foremost from the standpoint of cost effectiveness. The resulting increased formulary restrictions will impact large areas of branded portfolios, but the degree of formulary pressure will vary by therapeutic area and health channel.

How analytics can help: Pharma companies need to take this new reality into account – modeling to what extent their new and in-market products will be impacted. Insurers are likely to tighten cost/benefit analysis, creating a tougher path for second and third lines of treatments.

Insurance companies can use analytics to take a more holistic view, looking at the balance of cost efficiency and treatment effectiveness in the longer term.

Acceleration of Rx to OTC switches:

OTC switches will continue to grow and take a big bite out of the current Rx market.

Key drivers of OTC growth include insurance / reimbursement changes and pressure on prices.

Many Rx blockbuster products are in the late stage of their lifecycle, coming off-patent and losing 90 percent + of their volume to generics right away. The OTC switch offers a “second chance” for these brands. Finally, more informed consumers are increasingly interested in self-treating simple or chronic conditions

How Analytics can help: Robust models of Rx/OTC switches can help pharma companies understand the value of taking their brands to the OTC market. Consumer analytics can identify the segments that OTC brands should focus on, as well as help to determine pricing that will best protect their brands from private label competitors.

Doing More With Less:

Healthcare organizations today are constantly pressured by contradictory goals: to see more patients in less time, but become more patient-centric; and cut costs while improving quality and outcomes.

At the same time, it is clear that the industry is plagued by entrenched inefficiencies and suboptimal processes.

How Analytics can help: Optimization analytics can help make processes more efficient: locate resources (ambulances, clinics, etc.), optimize schedules of doctors and nurses, optimally route delivery vehicles, and assign spatially located patients to less busy medical facilities. Shift from blockbuster to highly targeted drugs:

Successful pharma companies will have to change their “blockbuster” and “high margin” mentality and rely on having more products, with lower revenues and lower costs.

That means having to overhaul the entire business model starting with a more nimble drug discovery and R&D process and ending with the ability to effectively target more fragmented “specialist” networks.

How Analytics can help: The advent of “big data” and the advanced analytical methods and technologies used to interpret it is a trend with the potential to revolutionize biology, medicine, and health care. As new types of data and tools become available, a unique opportunity is emerging for smarter and more effective discovery, development, and commercialization of innovative biopharmaceutical drugs.

Looking forward we can expect that in the next five to ten years the Healthcare industry will undergo monumental changes. ACA related changes (direct and indirect) are estimated to have a negative 10-15 percent impact on Healthcare revenues and at least double that (20-30 percent) on gross margins. To survive and navigate through these unprecedented changes and complexities, many Healthcare companies will need to find a new path forward, as well as cutting edge management tools to stay on that path. Advanced Analytics capabilities will become increasingly important in these conditions, providing clarity, promoting objectivity, and enabling faster and better decisions.

A co-founder of 4i, Lana Klein leads the firm’s Growth Foresight practice. She is a recognized industry expert in developing unique client solutions combining advanced predictive analytics with deep business knowledge. Focused on the CPG and Healthcare industries, Lana has more than 20 years of experience advising clients on a broad range of analytics solutions.

Original:

http://www.predictiveanalyticsworld.com/patimes/predictive-analytics-health-care-helping-navigate-uncertainties-change/

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