The Problem of Sorting Human Problems

The Problem of Sorting Human Problems

“Triaging” Human Conditions

The word “triage” comes from the French word “trier”, meaning “to separate, sift, or select”. The concept of triage may have originated from the Napoleonic Wars. It was later adopted during World War I.

The use of an effective triage system during the time of war was critical and necessary. Assuming that all lives (bodies) are valued equally in the battlefield, how can the squad optimize the allocation of scarce resources (medical attention, medicine, beds/sanitary conditions, etc.) while under tremendous pressure? Those of you who practice Emergency Medicine might find this eerily familiar even today.

Basic battlefield triage systems hence dictated the following classifications:

  • Those likely to survive, regardless of care received;
  • Those unlikely to survive, regardless of care received;
  • Those whose outcome can be significantly influenced by the care they receive. [This is where they’d direct most of the resources]

Rudimentary much? Indeed. In practice, this system is rarely followed. As you could probably tell, not all lives are treated equally in the battlefield (consider commanders vs. infantry units) and making a call on the “likelihood of survival” is…well, more of an art than science. Those in commanding positions could even prioritize the allocation of medical resources towards specific outcomes (preserving manpower vs. optimizing able-bodiedness).

Rise of the Emergency Room as “Catch-all” of Health Care Systems

Let’s fast-forward several decades. By the 1960s, health care delivery systems in the U.S. (and elsewhere) had evolved drastically. Patients are covered by 3rd party payors. Hospital-focused care delivery systems took prominence and patients with acute problems were directed to a special area called the “emergency room” (before there was the ER). As the demand for acute care increased drastically, the need to efficiently allocate hospital’s limited resources became increasingly critical. By the 1980s, emergency service had become one of the most important channels for patients accessing the health care system – a “catch-all” funnel, if you will. In fact, the U.S. Congress became so concerned about widespread reports of “patient dumping” that they passed the Emergency Medical Treatment and Active Labor Act (EMTALA), which has had a profound regulatory impact on all aspects of emergency care.

So where are we with Emergency Medicine today? Well, here’s a few facts:

Demand >>> Supply. Americans make 130 million visits to the Emergency Room annually, or roughly 42 visits for every 100 people we have here in the U.S. Take that in for a second. This has been a growing trend years in the making, and at the same time, the number of hospitals operating Emergency Departments has declined from more than 5000 in 1991 to fewer than 4000 in 2006. [Source] Why are EDs closing down if there’s such a need, you may ask? As it turns out, the ED business may not be entirely financially viable for many organizations. [Source]

Utilization by Specific Demographics. Among all those who utilize the ED, ED utilization rates are especially high among infants, people age 75 and over, nursing home residents, the homeless, African Americans, and individuals covered by Medicaid/SCHIP. [Source]

Annual ED visits by high-use population groups, 2006.

Complexity & Bias.
Patients’ perceptions of the urgency of ED visits often differ from the judgment of clinicians. In a study by Gill, et al. conducted on patients in the waiting area of an urban teaching hospital, they found that 82% of patients classified by triage nurses as non-urgent believed that their condition was, in fact, urgent. Patients often come to the ED for a variety of complex and overlapping concerns that include the need to quickly relieve pain or discomfort and “making sure everything is OK.” Caretakers of young children express additional concerns. Some feel they need professional reassurance to deal with their child’s inability to express pain and other symptoms precisely. Others want to make sure they are not to blame for the child’s problem. Some describe their choice to seek immediate verification of non-urgency as a form of parental responsibility. As strange as it sounds, this problem creates an inherent demand for rapid confirmation and reassurance.

A Whole Lot of Waiting. Think about a time when you or someone close to you were admitted in the ED. How was it? Patients spend a lot of time waiting in the ED. On average:

  • You would wait for 24 minutes before you ever see a provider;
  • If you break your bone, well, tough luck, it’d take 54 minutes before you get your hands on any pain medication;
  • If you finally got to see a provider and they decide that you should be admitted to the hospital, it’ll take 96 minutes before you actually get wheeled to the room;
  • If you’re lucky and they decided that your condition isn’t serious enough to be hospitalized, you’d be hanging out in the ED for 135 minutes before you could go home.

There’s a host of findings in the Synthesis Project conducted by the Robert Wood Johnson Foundation, which I’d highly recommend for those of you interested in this problem space. Here’s also a separate report from the Heritage Foundation on a similar topic.

Evolution of Triage Systems

As Emergency Medicine evolved over time, so did medical triage systems. During that time, many medical triage systems were developed, tested, and adopted worldwide. In a review paper published back in 2010, Christ, et al. attempted to evaluate four of the most prominent triage systems in existence – Australian Triage Scale (ATS), Canadian Triage and Acuity Scale (CTAS), Manchester Triage System (MTS), and Emergency Severity Index (ESI) – based on parameters of “validity” and “reliability”. While MTS and ATS touted a “three level” triaging schema, CTAS and ESI emphasized the need for more granularity in a “five level” triaging schema. If we leave the debate of “effectiveness” aside, I’m sure we can all agree that the “sorting of human conditions” is a pretty challenging problem.

Let’s highlight the fundamental premise behind why triage systems are needed:

The challenge we have on hand, and for the near future, is to develop an effective triage system, which, as their primary function:

  1. rapidly identify those who require the best available response for the medical problem presented, and
  2. focus the response using a sensitive and specific system, in order to
  3. use limited resources most effectively, and to
  4. focus the delivery of those in need of care to “centers” where definitive care can be given in one move.

So What?

So far, we’ve established a few key assumptions:

  • People look towards the ED as a primary way to get acute care they need;
  • ED overcrowding is a real issue;
  • The ED cannot, and should not, be the “catch-all” function for patients seeking care.

The real issue we have on hand isn’t “fixing the ED”. You can have all the ED in the world and still end up with a massively expensive, non-scalable, problem on your hand. It’s about directing individuals to the appropriate provider of care where definitive care can be given before these individuals self-triage themselves into the ED.

I’m not talking about the cases where the ED would be a clear destination (say, you found a pole that pierced through your lung). It’s about the cases where there isn’t a clear line to be drawn.

In an ideal world, there should be a centralized “command center” which directs individuals in a finite set of population to the right level of care. Patients should have their chief complaint fully addressed with a singular touchpoint, and in cases where follow-up is required, there should be a clear mission to address the remaining issues in the follow-up. This should all take place before anyone touches the care delivery layer (including the ED).

From the system design perspective, this may look like a massive optimization problem for the time between the presentation of chief complaint and resolution of the said complaint:

  • Minimize system cost (resources) incurred;
  • Minimize time to care delivery;
  • Minimize time to resolution of chief complaint;
  • Minimize touchpoints/referrals required;
  • Maximize patients’ need for trust and reassurance;
  • Maximize convenience to patients;
  • Maximize perceived and measurable value to patients
  • etc.

And that’s why this problem is interesting for many of us.

How do you get this right?

What a Gaming Platform and Healthcare May Have in Common

What a Gaming Platform and Healthcare May Have in Common

For those who are not familiar with Steam, Wikipedia offers a fairly good definition:

Steam is an Internet-based digital distribution platform developed by Valve Corporation offering digital rights management (DRM), multiplayer, and social networking. Steam provides the user with installation and automatic updating of games on multiple computers, and community features such as friends lists and groups, cloud saving, and in-game voice and chat functionality.

As a fairly avid casual gamer myself, I’ve found Steam to be increasingly effective in not only game distribution, but ever more so in creating an entire ecosystem of gamers and developers with incredible self-sustaining economics. In fact, the economics and distribution power of the platform/community have grown so much over the years that Steam can now be considered as one of the top (and few) places where independent game developers can garner a sizeable following of fans. For a bit more of the company’s history, click here.

As much as I’d like to rave about Steam, this post is meant to be a healthcare story.

There are several reasons for me to highlight Steam as a platform for what I envision as a potential prospect of our future healthcare solutions.

So the way it works today is that you’d first download the Steam platform onto your desktop. You’d have the ability to browse their store of games, purchase a game, download and install the game, then play the game. Pretty simple workflow, right? What’s interesting in this model is the ability for gamers to readily and quickly browse Downloadable Content (DLCs) as well as Community Assets (from Steam Workshop) which will enhance your gameplay in different ways.

Let’s take this analogy and extend it into the healthcare arena.

Suppose that you are a healthcare executive (“gamer”) who is making important decisions on a day-to-day basis. You are inherently interested (and excited) to figure out the best ways to solve the issues your organization is experiencing and/or are interested in solving (“type of game”). To that end, you’d like to browse for your options (“choosing the game in the category you’re interested in”) and this is where you hit your first roadblock – the “games” are distributed across all sorts of platforms…wouldn’t it be nice if most of what you need are aggregated on a centralized platform like Steam?

This is not a big issue, however, because as consumers of these products, I’m sure you’d find plenty of ways to get the “games” you want to play. Now, what is important is what follows the purchase of the games.

The reason the Steam-like platform is so attractive is the intrinsic ability for gamers (in this case, healthcare executives) to quickly and easily browse feature enhancements (DLCs and Assets) which builds upon the base game (in this case, base healthcare solution). The ability to expand the base solution’s capabilities is immense and truly leverages a community’s effort. This is important because we must recognize that a “healthcare analytics solution” isn’t the end-all-be-all for healthcare executives. If we look at the journey from data to outcomes, we can see that oftentimes, the point solutions we prescribe to only gets us halfway there. The few end-to-end solutions are far too bulky to be played around with.

Let’s for a moment consider the following. Instead of buying a game with ALL of the features and expansions (end-t0-end solutions) or a game with only base features (point solutions), what if we position the game as an underlying engine which additional features and enhancements can be readily coupled upon? You take your data, feed it through this base engine, couple on a few feature enhancements to further process the data, then couple on some workflows to render your insights in a much more consumable way than ever before.

What I’m suggesting here isn’t a call for more APIs. We’ve got plenty of that and it seems as though everyone’s working on one. To complete the journey from raw data to true health outcomes (what you’d actually pay for), we’ll need the engine, the feature enhancement, as well as custom workflows to work seamlessly together. You, as the end user, should have the ability to browse an entire inventory of any of these components mentioned above and pick your own “cocktail” specific to your needs.

And oh boy, we’re just getting started here. Now mix in the business model conversation – which parties would you like to involve in both the creation as well as the enhancement of any of the components mentioned above? In the Steam model, gamers typically would pay for the base game and the DLCs, but would have free access to most of the community assets (created by fellow community members). Can this model be similarly replicated to our case here?

I’d imagine this post as an initiation of this conversation, and I’d love to welcome my fellow colleagues/peers’ feedback and thoughts.

The Transforming Payer Industry – A Case for Deeper Analytics and Broad Strategic Alliances

The Transforming Payer Industry – A Case for Deeper Analytics and Broad Strategic Alliances

The [payer] industry is truly on the cusp of a new era where payers are transforming from being seen primarily as claims payment entities to being seen as true partners for patients and providers.

Source: Nancy Fabozzi, Frost & Sullivan

At our current line of work, we’ve come across and became intimately familiar with how the changes in the healthcare policies (ACA, etc.) have impacted the way stakeholders look at data, analytics, and novel methodologies in the healthcare space. Yet, given our line of work (which heavily focuses on the provider and consumer space), the nuance of how these policies affect the payer space has always escaped me. Nevertheless, I was fortunate to have come across Nancy’s articles (published in HIMSS’ Health IT Pulse) to get myself acquainted with a quick overview of this transforming industry. My hope is to summarize the findings which Nancy has finely articulated, as well as to drill a bit deeper into many of the concepts that should be elaborated further.

After reviewing the materials, I found the story for the changes in the payers market to be awfully similar to what we’ve come to understand in the provider and consumer space. In fact, the beauty is that we’re now finally seeing the overlap/crossover of interests and incentives tied to all of the stakeholders involved. It’s a true partnership with one end goal – to deliver the best and most affordable care possible to the end customer.

Primary Drivers for the Need of Payer Analytics

  • DRIVER 1: Need for managing new levels of risk resulting from the ACA
    • Why? 
      • There are 18 million Americans previously uninsured who will hit the market. These individuals are largely uninformed and has little to no experience in using the health care system. [Source, see page 17]
      • The ACA has implemented key provisions to intrinsically address “Adverse Selection”. The provisions mandate a single risk pool, which dictates the need for payers to have a better understanding in managing and modeling their pool of risks. [Source, see page 9]
  • DRIVER 2: Need for reporting and compliance for two key National Committee for Quality Assurance (NCQA) quality initiatives: HEDIS and Star Ratings
    • Healthcare Effectiveness Data and Information Set (HEDIS) is a tool used by more than 90 percent of America’s health plans to measure performance on important dimensions of care and service. Altogether, HEDIS consists of 81 measures across 5 domains of care.
      •  Measures “Health Plan” performance
      • Why Care? The Centers for Medicare & Medicaid Services (CMS) have directly linked reimbursement for healthcare services to patient outcomes.
    • Five-Star Quality Rating System
      • Compares Medicare Advantage Plans and Prescription Drug Plans
      • Why Care? The Affordable Care Act of 2010 mandates that CMS make quality bonus payments (QBPs) to Medicare Advantage (MA) organizations that achieve at least four stars in a five-star quality rating

Analytics solutions needed to drive excellence in quality metrics must be capable of integrating claims, operational, and clinical data…the need to access and incorporate various unstructured data elements is becoming more critical, further driving the need for next-generation analytics solutions.

  • DRIVER 3: Need for refined analysis of the depth and breadth of data to drill down on cost drivers – condition, member, physician, and hospital levels.
    • Payers are moving beyond their historic focus on retrospective utilization review to taking a more proactive role in engaging and supporting providers with data for population health.

Payers will need a holistic solution that not only encompasses a breadth and depth of data, technical capabilities, as well as a series of supporting infrastructure to achieve these goals. There are very few solution providers that can offer all of these capabilities, hence the call for broad strategic alliances in this space.

Top Three Challenges for Payer Analytics

  • Data Integration
    • Data integration is at the heart of any payer organization. With the amount of M&A activities taking place and the focus on population health management, payers will need to truly integrate the data coming from disparate sources (e.g. hospital, provider, and allied health professional EMRs, claims files, patient monitoring devices and wearables, etc.)
  • Data Governance and Data Quality Control
    • In most payer organizations, business functions are still very siloed. In order to generate the meaningful analytics insights dictated by business needs, there needs to be a new imperative from a strategic, top down perspective. Breaking down these silos will be a challenge in itself, but it must be done.
  • Combination of Clinical and Claims Data
    • Today, payers and providers still largely don’t know how to effectively work with each other to share data. This presents an opportunity for third-party entities that can extract data from both payers and providers, shifting through the data and run predictive modeling and other analytics functions to support population health and other initiatives.

Without doubt, this will be a whole set of daunting tasks. But payers must step up to the challenge, and solution providers must work together to address this critical need for the payers and its respective stakeholders.

Select Vendors Serving the Payer Analytics Market

  • Platform Analytics Solutions
    • IBM
    • TriZetto
    • Verisk Health
    • ZeOmega
    • QlikView
    • Optum
    • SAS
    • Teradata
    • Inovalon (formerly MedAssurant)
    • Ab Initio
  • Point Solutions
    • SAS
    • Optum
    • MEDai (acquired by LexisNexis)
    • 3M
    • “homegrown solutions”
  • Analytics Services Vendors
    • MEDai (acquired by LexisNexis)
    • Optum
    • Teradata
    • Truven’s CareAnalyzer
    • Verisk Health

Optum is uniquely situated across each of the above solution category, which is perhaps indicative of this vendor’s breadth and reach across the payer space. It’d be extremely interesting to see how the other players can potentially work with one another to fulfill an end-to-end need in the payer analytics market. It will be valuable for the players to conduct a “horizon scan” of the current competitive market to understand where the potential gaps and opportunities are.

Interesting Partnerships

  • Optum Labs
    • Optum Labs is a new kind of open collaborative center for research and innovation involving all stakeholders (on improving patient outcomes and containing healthcare costs.
    • Originally set up between Optum and Mayo Clinic, the organization has grown to a very significant collection of partners and collaborators.
    • 4 Tenets: Collaboration, Data & Analytics, Prototyping & Testing, and Adoption.
  • HealthCore + Wellpoint + AstraZeneca

A Brief History of Universal Health Care Efforts in the United States

A Brief History of Universal Health Care Efforts in the United States

Source: Karen S. Palmer MPH, MS, Physicians for a National Health Program (PNHP)

Brief Summary

  • US circa 1883-1912
    • Matters left in hands of States, which in turn, left them in hands of private industry and voluntary programs
    • Lacked critical mass to make it into the national agenda
  • AALL Bill, 1915
    • In 1906, the American Association of Labor Legislation (AALL) led the campaign for health insurance.
    • Coverage. The Bill limited coverage to the working class and all others that earned less than $1200 a year, including dependents. The services of physicians, nurses, and hospitals were included, as was sick pay, maternity benefits, and a death benefit of fifty dollars to pay for funeral expenses.
    • Costs. Costs were to be shared between workers, employers, and the state.
    • Reception
      • AMA’s Position. In 1917, the AMA House of Delegates favored compulsory health insurance as proposed by the AALL, but many state medical societies opposed it. Due to disagreements on physician compensation, AMA withdrew its support.
      • AFL’s Opposition. President of the American Federation of Labor repeatedly denounced compulsory health insurance – to maintain union strength.
      • Private Insurance Industry’s Opposition. Bill’s coverage for funeral expenses would undermine the multi-million dollar life insurance industry’s existing business (coverage for funeral expenses).
  • WWI and anti-German fever, circa 1917
    • Compulsory national health debate tabled until 1930’s
  • The Committee on the Cost of Medical Care (CMCC), 1920’s
    • Self-created & privately funded by 8 philanthropic organizations including the Rockefeller, Millbank, and Rosenwald foundations to address concerns over the cost and distribution of medical care.
    • Position. CCMC recommended that more national resources go to medical care and saw voluntary, not compulsory, health insurance as a means to covering these costs.
    • Reception. AMA treated CMCC’s documents as a radical position advocating for socialized medicine.
  • FDR’s Attempts at Universal Health Care Provisions
    • First Attempt – Social Security Bill of 1935
      • Compulsory health coverage provisions were excluded from the Bill due to fierce opposition from the AMA.
    • Second Attempt – Wagner Bill, National Health Act of 1939
      • Never enacted due to unfavorable political environment.
  • Wagner-Murray-Dingell Bills: 1943 and onward through the decade
    • Aims to establish compulsory national health insurance funded by payroll taxes
    • Never passed, but generated extensive national debates
  • Truman Period, 1945
    • Truman was strongly committed to a single universal comprehensive health insurance plan.
    • Reception. AMA, the American Hospital Association, the American Bar Association, and most of then nation’s press hated the proposed plan.
    • Resistance. In 1945, the AMA spent $1.5 million on lobbying efforts which at the time was the most expensive lobbying effort in American history.
    • Outcome. Truman’s plan died in congressional committee.
    • Causes for Failure. Interest group influence, ideological differences, anti-communism, anti-socialism, fragmentation of public policy, the entrepreneurial character of American medicine, a tradition of American voluntarism, removing the middle class from the coalition of advocates for change through the alternative of Blue Cross private insurance plans, and the association of public programs with charity, dependence, personal failure and the almshouses of years gone by.
  • Johnson and Medicare/caid – Victory(?) at Last!
    • Rhode Island congressman Aime Forand introduced a new proposal in 1958 to cover hospital costs for the aged on social security.
    • The AMA countered by introducing an “eldercare plan,” which was voluntary insurance with broader benefits and physician services.
    • Concessions were made:
      • to the doctors (reimbursements of their customary, reasonable, and prevailing fees)
      • to the hospitals (cost plus reimbursement)
      • to the Republicans created a 3-part plan, including:
        • the Democratic proposal for comprehensive health insurance (“Part A”)
        • the revised Republican program of government subsidized voluntary physician insurance (“Part B”)
        • Medicaid
    • Finally, in 1965, Johnson signed it into law as part of his Great Society Legislation, capping 20 years of congressional debate.

An Aging Nation: Are We Ready for This Yet?

An Aging Nation: Are We Ready for This Yet?

I believe this is a topic that is near and dear to our heart for many of us. If not now, it will be. I was initially faced with this problem as we brought my grandparents over from China to stay with us here in the U.S. so that we can better take care of them. This construct, however, is highly cultural in nature. As we came to learn, there is a whole range of solutions which we, as Americans, have created to address the needs of our aging loved ones, and little of which we truly understand.

So What’s the Big Deal?

Between 2012 and 2050, the United States will experience considerable growth in its older population. In 2050, the population aged 65 and over is projected to be 83.7 million, almost double its estimated population of 43.1 million in 2012.

Source: US Census Bureau

The impact of this trend will be significant. Longer life expectancy for an increasingly aging population will further put the healthcare system we have today under duress. According to HHS & DOL, 27 million Americans will require some type of long term care by 2050. In response, between 5.7 million & 6.5 million nurses, nurse aides, home health & personal care workers will be needed to care for these individuals.

From a pure economic perspective, we would either need to task the nation to increase the supply of caregivers through a series of incentives and programs, or reduce the demand for such services – an implication for better management of chronic conditions for the elderly. This is particularly the reason that concepts such as “assisted independent living” and “aging in place” have been able to garner a sizeable following, for very different reasons.

Regardless of the “vehicle” considered in this framework, there are some key questions which we’ve asked ourselves:

  • Who is going to take care of our grandparents?
  • Do we trust this person? On what capacity?
  • Where should our grandparents live?
  • How should we manage our grandparents’ chronic conditions?
  • What options would our grandparents prefer, yet at the same time, options we’re comfortable with?
  • Can we even afford these options?

These are just the beginning of the questions we found ourselves asking, and the beauty…as well as the challenge, is that we will likely have all different kinds of answers. So how can you, perhaps as a provider organization, possibly solve a healthcare delivery problem when there are such personal motivations involved in the decision-making process?

In my work at Watson, we’ve come across multiple stakeholders who are looking at this problem from a variety of angles. We’ve come across innovators in the space who are trying to implement a set of remote monitoring capabilities to better equip caregivers in addressing the needs of the elderly population, typically in a specific social construct (long-term care facility, for example). Sensors, analytics, dashboards – all the bells and whistles. We know the folks at Aging 2.0 who are working very closely and diligently with their cohort of disruptors trying to change the game in this space. Shoot their co-founder, Stephen Johnston, a quick tweet, if you’d like to learn more.

On the other hand, as we engage with the government entities abroad, we realized that this is a trend/problem felt by most developed (and even developing) nations.

Case in point: Singapore.

By 2020, manpower needs in the Intermediate and Long-Term Care (ILTC) sector, will grow by about four times, from the current 4,000 to about 15,000. Singapore will need more healthcare staff across all levels, from nurses, therapists, medical social workers to healthcare support workers.

The Government will invest in building up the capacity and capability of the ILTC sector, including its ability to attract and retain staff. For a start, up to $32 million will be channeled into manpower initiatives such as pay enhancements for the healthcare professionals in the sector and enhanced staffing for community hospitals and nursing homes in FY2012. These initiatives are expected to support the sector in attracting and retaining more quality staff.

Source: Singapore Ministry of Health, 2020 Roadmap

Feel free to “drop in” on any nation’s healthcare ministry roadmap – you’d find a similar emphasis.

Coming back to the equation (supply vs. demand), we would naturally ask whether and how technology can play a role in this. Here are the questions I’d leave you with:

  • Can technology help us scale the necessary expertise in order to virtually replicate and increase the supply of care-provision?
  • Can technology help us identify, stratify, monitor, and advise for those who suffer from chronic illnesses in order to reduce the demand of care-provision?

Of course, as we know, the topic of health is, and should never be, so simple and one-dimensional. However, these are the questions we should get started with, and I know many are already working on solutions in answering these questions.

Relationship between Comparative Effectiveness Research (CER) Studies and Pharma/Life Sciences Industry

Relationship between Comparative Effectiveness Research (CER) Studies and Pharma/Life Sciences Industry

One of the many nuances that has always escaped me is the direct (and indirect) relationship between Comparative Effectiveness Research (CER) Studies and Pharma/LS industry. Why do CER studies mean to Pharma and what are some of its implications in how Pharma stakeholders conducts its business?

CER is the conduct and synthesis of research comparing the benefits and harms of different interventions and strategies to prevent, diagnose, treat, and monitor health conditions…CER can provide patients, providers, payers, and other stakeholders with the information to improve decision-making about treatments, coverage options, and other issues affecting health care quality and outcomes.

Source: National Pharmaceutical Council (NPC)

Comparative Effectiveness Research is not a novel concept. However, with recent changes in policies (American Recovery and Reinvestment Act in 2009 and PPACA in 2010), significant attention has been directed to the need for Patient-Centered Outcomes Research.

In 2009, $1.1 billion of President Barack Obama’s stimulus package was earmarked for CER.

The passage of PPACA, in turn, established the Patient-Centered Outcomes Research Institute (PCORI), which oversees and sponsors CER in the U.S. and provides health care decision-makers with current and relevant data that can enable them to make more informed decisions.

Funding Scheme

In 2015, PCORI has an approved budget of $462.8 million.

Since it first began approving research awards in 2012, PCORI has committed a total of $549 million in support for comparative clinical effectiveness research (CER) and related projects.

Combined, NIH and AHRQ had nearly $675 million in funding for CER in 2014.

Current Health Care Decision-Making Environment

Recent survey conducted by NPC reported the following findings based on 122 key deicison-makers in the space:

  • Research Standards: There is a growing movement toward widely agreed-upon research standards (49% consensus)
  • Research Priorities: Many felt that research priorities somewhat/adequately reflected treatment choices in 2015 (41% consensus)
  • Transparency: Slightly less than half of respondents felt that there is no or little transparency in evidence evaluation
  • Treatment Assessments: Value of treatments remains narrowly focused on only clinical effectiveness (58% consensus)
  • Integrated Purchasing of Health Services: Nearly 2/3 of respondents feel that purchasing of health services trends toward a siloed view
  • Outcome-Based Contracting: Most felt there is little to no outcomes-based contracting (70% consensus)
  • Completeness of Comparative Effectiveness Evidence Base: There is not enough evidence available to answer treatment questions (67% consensus)
  • Use of Real-World Evidence: Real-world evidence is limited in decision-making (44% consensus)

How Do CER Studies Relate to Pharma / Life Sciences Stakeholders?

This is the central question I originally embarked on to dig deeper. After reviewing multiple pieces of the literature available, I had come across an opinion piece by Michael Abrams, managing partner at Numerof & Associates, Inc., who had succinctly articulated the linkage:

The ultimate impact of each of these changes for pharma will be a redefinition of value. To fully identify the potential value of products, manufacturers will need to look beyond product attributes, such as physician ease of use. Instead, they will need to evaluate new products on how they potentially improve current treatment regimens for the condition addressed, save costs and improve outcomes. For comparative purposes, determining the potential of a new drug to take the place of multiple drugs in a current therapeutic regimen could be important. Demonstrating equivalent efficacy (vs. superiority) may be sufficient—and of interest to payers—if the product has an improved dosing frequency (and improved patient compliance) or some other benefit, such as reducing the number of medications required for treatment or the number of physician visits.

Source: Pharmaceutical Commerce

In short, the changes in reimbursement scheme will require for most stakeholders in HC, pharma included, to fully consider the direct impact of products on patient outcome. This shift necessitates an infrastructure for: 1) normalized data lake containing anonymized patient population data, 2) a closet full of tool kits for the positioning, processing, and evaluation of data, and 3) a virtual environment safe and efficient enough for conducting (1) and (2).

This brings us, once again, for the need for deep analytics in the Life Sciences industry that leverages Real World Evidence.

The Case for Analytics in Pharma/Life Sciences Industry

The Case for Analytics in Pharma/Life Sciences Industry

As I re-familiarize myself with the LS/Pharma industry – specifically the use of Analytics for LS/Pharma, I’d like to share some assets that I’ve come across with you (for your pleasure reading and curiosities if you haven’t seen them already). I’ll summarize these findings/key stats for the ease of consumption.

Basis: See attached Analytics in Pharma and Life Sciences – Everest Research Group

Key Drivers for Analytics in Pharma/Life Sciences Industry

    • Compliance
      • Evolving stringent regulatory environment
        • Introduction of PPACA and PPSA, directly imposing changes in reimbursement mechanism and transparency.
        • Dr. Daemmrich (HBS) has a good paper on the impact of such policies on the Pharma Industry.
      • Rising cost of compliance
        • CMS estimates that the cost of compliance for the Sunshine Act (under PPACA) would be US$269 million, with subsequent years costing US$180 million to the broader pharma and life sciences industry.
      •  Significant risk of non-compliance
        • Timely and accurate reporting is critical for the Sunshine Act, as non-compliance penalties range from US$10,000 for each payment not reported (subject to a maximum of US$150,000) to US$100,000 for knowingly withholding information (capped at US$1 million).
    • R&D Productivity
      • Rising R&D expenditures
        • The crisis of R&D is highlighted in a new report by the Tufts Center for the Study of Drug Development. Back in 2003, the Tufts team estimated that the cost to research and develop a new drug was $802 million (in 2000 dollars). In 2013 dollars, that would be $1,044 million.
      • Falling FDA approvals
        • Source: Nature Biotechnology
        • The global life sciences sector’s general decline in R&D productivity is a frequent topic of conversation among industry stakeholders, investors, and analysts. Total projected value of late-stage pipelines for the 12 largest pharmaceutical companies showed a decline from $1,369 billion to $913 billion in 2013.
      • Static/Deteriorating health outcomes
        • Adverse event reports to the FDA increased at 4% CAGR from 2003 to 2011.
        • Serious/Death outcomes increased at 16% CAGR from 2003 to 2011.
    • Value Chain Digitalization
      • Increasing cloud-based services and mobility solutions
      • Proliferation of social media
        • Proliferation of media in general, and social media in particular, is increasing the public scrutiny on the industry.
        • Examples include: Patients Like Me; Treato
      • Innovative and customized delivery
    • Profitable Growth