Diagnosis and Care Planning – Value Stream Considerations

Diagnosis and Care Planning – Value Stream Considerations

Diagnosis and Care Planning

Elements of Value in Oncology Care

Value Stream Process Flow Considerations for

Patients, Employer/Payers, Providers and Private Equity

May 24, 2021

Dr. Kashyap Patel & Wes Chapman

Introduction – Elements of Value in Oncology Care

This is the first in a series of blogs looking at value-based medical oncology care, specifically addressing the key clinical/process elements in a value-based program that create value for the patient and the rest of participants in the care ecosystem. Our analysis is focused on macro-level processes because these are the ones that get the least scrutiny. As examples, the Oncology Nursing Society (ONS) does a terrific job designing and updating detailed operational processes at the practice level. CMS and NCQA provide clear operating direction for process through the requirements of the OCM and Patient Center Specialty Practice, respectively.

We kick off looking at the macro level value for three fundamental processes – screening, diagnosis, and care planning. These are the key steps for identifying, understanding/classifying, and planning the care for a cancer patient. Our analysis relies on the constructs of lean and six-sigma value-stream mapping (VSM), but at a macro level, generally following the initial constructs of Russell Westcott and the later work of Mark Graban (here). In all value application of VSM, the focus is on defining those steps that add value on one hand, and the delays, excess capacity and excess costs that detract from value on the other.

There is one critical difference between production-oriented value stream mapping and healthcare. In a normal production environment, you get paid for the product at the end. In healthcare you get paid by a third party for each step in the process, with that payment controlled by the rules and dictates of the third-party payer.

If providers in the process stream want to get paid, they need to focus on the detailed conflicting and loosely defined requirements for each payer at each process step. These payer relationships define the limits of the possible in oncology care, and create an operational myopia, where what is possible in care delivery is strictly limited to the payer construct. Accordingly, we will look at the obvious waste created by opportunity cost as well as delays and excess cost introduced by payer requirements.

One important corollary point about the determination of payment by a third party insurance payer, is that the amount of payment effectively caps the amount of work available at that step. As an example that we will explore more fully later in this paper, the process steps of diagnosis and care planning – critical steps of value creation – are dramatically underfunded by commercial payers. Conversely, large self-funded employers are investing most heavily in these steps, recognizing the value that they create for patients and the potential waste they can help avoid.

Accordingly, we will highlight common patterns of waste, delay or information misuse that do not benefit the patient or create value – the hallmarks of value-stream analysis. But we also include clearly addressable issues of disparities in care based on social and racial constructs. Disparities in equity of health care cost our society an estimated $230 billion per year (here) and are therefore perhaps the single largest issue of waste, time loss and needless human suffering in our analysis.

Our hope is to stimulate discussion to create paradigm for value stream mapping common to most medical oncology cases and useful to process improvement throughout the ecosystem.

Shown below is a simple process map for the processes that we are considering:

Figure 1


Cancer Screening – Changing Methods and Meaning

Cancer is everywhere and always a disease caused by errant genes – normally based on mutations developed over the course of a lifetime (somatic cells) but occasionally inherited directly from parents in germ line mutations (5-10% of all cancers). Over the last 30 years cancer detection has progressed from pap smears, biopsies, mammograms, and x-rays; to MRIs, CTs, PET-CTs and liquid biopsies. This technological transition has dramatically increased the breadth, accuracy, and specificity of screening, but at much greater cost. Earlier detection has facilitated earlier treatment and the marked improvement in survival over the last 10 years.

Changes in individual behavior (mainly smoking reduction), improved screening, earlier detection and improved drug therapies have all contributed to meaningful decline in cancer deaths totaling 29% from 1991 to 2017, and 2.2% from 2016 to 2017 alone (here). These improvements are not uniform across our society – with generally poorer survival statistics for minority groups. As an example, the 5-year relative survival rate for all cancers combined that were diagnosed during 2009 through 2015 was 67% overall, 68% in whites, and 62% in blacks.

Figure 2

Deaths from colorectal cancer by population group. White circle is baseline 2007, red (missed target) or blue (achieved goal) from 2017 (here)

Despite this progress, the US Preventive Services Task Force noted in a paper on January 25, 2021, “the USPSTF often finds substantial data that potential lifesaving benefits of recommended services are not equitably available to Black, Indigenous, and Hispanic/Latino people. For instance, the 2020 systematic review to inform modeling for the USPSTF colorectal cancer screening recommendation found consistent evidence of inequities across the screening-to-treatment continuum that encompassed access to screening, quality of screening, time from diagnosis to treatment, and quality of treatment.”, (here).

In addition to racial disparities, there are very real issues related to coverage by private insurance, and the problems faced by the uninsured. With the growth of high deductible plans in recent years we have seen a gradual decline in screenings for younger patients covered by private insurers, which dramatically increased during COVID-19 pandemic.

Figure 3

Screening for colorectal cancer by population group. White circle is baseline 2007, red (missed target) or blue (achieved goal) from 2017 (here).

According to the CDC, the result is generally poor compliance by Americans with recommended screening protocols, highlighted by only 35.4% of uninsured women getting breast cancer screening (here). If the screening process fails, patients are sicker when finally diagnosed, costs increase and survival decreases.

Screening Analysis – Value Stream Mapping Considerations

  • The value of cancer screening: Cancer screening is the fundamental first step in finding and addressing cancer in the US. It is supported by the CDC, the American Cancer Society, the NCI, and the North American Association of Central Cancer Registries, and operates based on recommendations promulgated by the Congressionally mandated US Preventive Services Task Force (USPSTF). Effective screening has been a central part of the success in the improvement in cancer survival in the US in the last 20 years.
  • Time and cost considerations of value creation (screening delivery): All the major screening techniques take less than 30 minutes to deliver, and interpretation of the results takes less than 30 minutes. Including patient transportation, none of these is more than half a day of patient time. Schedules of screenings designed by the USPSTF are optimized to balance cost and risk adjusted incidence expectations. Total delivery time of 1 hour and marginal cost of $250 is what we will use in our value stream analysis.
  • Time and cost considerations of waste and excess cost in screening delivery: Based on the results referenced above, we estimate that at least 30% of the targeted US population does not receive timely or appropriate screening for cancer. Financial cost to the system and patient for screening depends on payer (if any), location of test and test performed. Based on insurance, patient cost can vary from $0- thousands of dollars for any individual test. Costs for the same test vary by at least 757% (here) between institutions. Inefficient primary care delivery for many target populations increases the difficulty for patients to access tests, to understand test results and to access follow up care as needed. The result is impossible to quantify or reasonably estimate but includes more cost to treat much sicker patients later in disease progression.
  • Opportunity cost – data and analytics: The data collection system currently in use for nationwide data collection for cancer is manual in central cancer registries (CCR) is based at the state level, was mandated by Congress in 1992 (here), and built on a system dating back to the Nixon Administration. Data is late (up to 24 months), incomplete and only relates to the limited population tested. It does not include any follow up for treatment to evaluate efficacy or safety and has no capability to incorporate the subsequent diagnostic genetic tests central to the understanding and treatment of cancer.
  • Opportunity cost – racial and socio-economic: The current screening system fails disadvantages populations based on race, economic status, and location (rural). The fundamental failing of access for these citizens results in sicker patients, worse prognosis, and greater economic cost to treat – when treatment is ultimately available.
  • Incompatibility with new technologies: The rapid development of liquid biopsy as a screening and diagnostic tool will blur the lines between screening and diagnosis. The ability to reliably detect circulating tumor cells (CTCs) and cell-free tumor DNA (ctDNA) will dramatically facilitate the integration of screening with treatment planning. This is a typical process step elimination through new capability, but will require new data and payment regimes to work.
  • Calculation to scale the size of the problem: Let us assume that inefficiencies in screening amount to 30% misses of serious cancers, yielding a miss of around 540,000 cancers per year. Caught early cancers have a much lower cost to treat than advanced cancers, and a much better prognosis. Let us assume that the delay in diagnosis for the 540,000 cancer patients missed in screening adds $50,000 per patient in costs – a conservative estimate. This puts the incremental costs for missed screening at $27 billion – plus the additional human suffering associated with advanced cancers.
  • Summary and conclusions: We feel that the current screening process for cancer is dictated by the interaction between payer and providers rather than a process designed around patient care. Our current system of cancer screening produces inadequate screening of the population with no follow up to ensure timely access and delivery of care. Critical elements of data capture are wholly lacking, impeding improved medical care, data driven scientific knowledge and value-based care metrics. Our system is inadequate for the prosperous and insured population, and wholly lacking for disadvantaged and minority populations. We strongly support a CMMI pilot program to test a CMS supported screening model on a state-wide basis, like the payment model for COVID-19 payment. It would be imperative to integrate data gathering and analytics through all the pilot sites to ensure integrated data capability and continuity from screening capture through treatment outcome.

Diagnosis and Care Planning (DCP) – integrated approach based on precision medicine

Over the last twenty years cancer diagnostic activity has transitioned from a system based on microscopic pathology of tumor biopsies and radiographic imaging, to a focus on genetic analysis and the use of increasingly effective targeted therapies, specifically designed to address genetically defined targets. Predictably, this has been driven by concurrent advances in genetic testing, yielding reduced costs for these tests, better capabilities to handle genetic diagnostic data and corresponding advances in drug design and development. The genetic testing has gradually developed from early models like flow cytometry to next generation sequencing technology (NGS), and finally to whole exome sequencing (WES), effectively moving from single gene tests (flow cytometry) to 50-300 gene panels (NGS), to the entire genetic code respectively (around 20 thousand individual data points describing the patients’ genetics).

Figure 4

Screening and diagnostic tools progress from microscopic pathology to NGS

Corresponding with the growth in genetic testing has been the growth in compounds targeted to those mutations. What began with targeting Philadelphia Chromosome -positive (Ph+) acute lymphoblastic leukemia (ALL) with imatinib has given rise to the precision medicine and immunotherapy today. The key considerations here are: 1) The effectiveness of targeted therapy and immunotherapy depend on accurate and broad-based genetic testing, 2) Precision medicine has driven the improved survival that we have seen in cancer over the last 20 years, 3) The cost of genetic testing has continued to decline over the last 20 years, with an estimate of $0.0072 PMPM for NGS for patients receiving NGS vs. multiple single gene tests (here),  4) Drug approvals for precision medicine have surged over the last 5 years by 185% to 57 drugs in 2020, including 18 novel compounds (here), 5) The pipeline of targeted drugs in late-stage trials in 2018 exceeded 750, up 19% from the prior year (here), 6) Over 450 immunotherapies are currently in all phases of trials, and 7) 3,702 industry sponsored active and recruiting oncology drug trials (phases 1,2 and 3) have started since 1/1/2019.

Figure 5

Growth of late-stage trials

Figure 6

Growth of Immunotherapy

Figure 7

Precision medicine dominates recent approvals

Diagnosis and care planning have developed into an integrated and iterative process, heavily reliant on NGS to understand and effectively treat the underlying cancer. Importantly, this has become an iterative process over the course of treatment, where unstable cancer genetics due to continuous mutations can require ongoing diagnosis and testing over the course of treatment.

Against the backdrop of this exploding volume of information and opportunity, we would expect that DCP would enjoy a continuous expansion of compensation by payers, and a commensurate time commitment by providers and other professionals. Unfortunately, this has not been the case. Compensation for all of these activities is typically rolled into the E&M codes related to the first patient visit, and runs around $200. That is right, payers are investing $200 for the DCP activities for decisions that may: 1) Either save a patient’s life, or not provide little benefit if misapplied, and 2) Cost hundreds of thousands of dollars. Is this the only way to do this.

The alternative case study of Walmart is very instructive (here). Walmart has adopted a center of excellence program with Mayo Clinic for the key DCP process. This involves sending their beneficiary cancer patient, together with a companion to the Mayo Clinic to execute the DCP steps. Their initial findings were instructive – around 55% of the patients had their care plan changed, and an additional 10% who had some fundamental problem with the essential cancer diagnosis (here). That is a 65% aggregate miss in the critical DCP process step. That is what $200 buys. We estimate that the process for DCP described by Walmart costs at least $5,000 – a $4,800 price differential. Walmart is regarded as one of the best purchasing organizations in the world, and for them it is worth the difference in price to ensure that the patient get the best care that addresses the patient’s disease. Put another way, we estimate that Walmart sees at least 25x more value in the DCP process than is being paid for in the market. And we think that they are right.

Further complicating the picture is that the explosion of new therapies and genetic targets is focused on an exceedingly small population of patients. Cancer incidence in the US is around .6% – around 1.8 million new cases per year. As shown in Figure 7 below, some of the most treated genetic targets affect ≤ 1 % of all cancers, and several have multiple possible drug therapies, including biosimilars in the case of HER2. Of the 57 oncology drug approvals in 2020, 30% were granted Orphan Drug status, highlighting the exceedingly small target markets for these drugs. According to the NCI (here) there are currently over 300 cancer drugs, many (mainly chemotherapy cytotoxins) with multiple uses, but increasingly dominated by new, expensive, effective drugs with extremely limited genetic targets.

Figure 8

Drug approvals by year by target mutation

Drug approvals are critical to the development of a formulary with sufficient breadth to targets to cover the major genetic targets for any cancer type, but this must correspond to mutations which can vary significantly based on race. This issue was highlighted both clinically and financially in February of this year in a Hawaiian lawsuit against Bristol-Meyers Squibb Co. and Sanofi SA in which a judge ordered the payment of $834 million in penalties related to the blood thinner Plavix. Plavix has been shown to have diminished or no effect for people of East Asian and Pacific Islander heritage, which was not disclosed to patients or in the marketing of the product.

Shown below are data related to incidence of prostate cancer by racial group, and the corresponding demographic makeup of trial participants – which does not correspond to incidence or risk.

Disparity in prostate cancer incidence and related clinical trial participation

Figure 9

(source ASCO Pubs)

Prior Authorization (PA) – Payer response to complex diagnostic and care planning challenges.

We will look at PA in terms of process flow. It is an external approval process normally run by a third party contracted by the payer. Oncology prior authorization is predicated on approved treatment pathways promulgated by NCCN and others and enforced adherence to those pathways. The primary purpose of PA is cost control. It has other potential benefits – PA is frequently described by PA vendors as ensuring patients access to latest therapies as accurately diagnosed and planned. It is, however, a monumental process impediment and routinely consumes up to 16 hours of provider and staff time per week – an estimated inefficiency of around $80,000 per provider (here) per year, at a total systemic cost well in excess of $1 billion – Ouch!

There is no conclusive evidence that it either improves care of saves cost – even in the narrow confines of a point solution (here). In process terms a point solution fixes one problem, while ignoring the total impact on the system which may be negative. We have not seen any study that considers the total cost of PA, or even one that compares formulary choice from “authorized patients” versus those that are not subject to PA in Medicare. A casual review of promotional literature from PA vendors indicates savings of 7+%, but without clarity about a suitable comparator.

A recent AMA survey captures the level of physician dissatisfaction with PA (here). Over 90% of surveyed physicians report care delays and 30% report that PA has led to a serious adverse event including 9% resulting in permanent harm or death. It is hard to consider such an obvious process block, designed as a structural impediment to orderly process flow, as anything but an intentional obstacle to patient care. In the case of cancer care this becomes a two-layer impediment – if physicians are not given access to advanced genetic testing, then it becomes impossible to determine suitability for targeted therapies. And patient care suffers while the resulting cost paradigm becomes indecipherable.

If the 30% delays on average generates 2 emergency room visits per patient, at a nationwide average of $2,000 per visit, these delays for 30% of the 900,000 newly diagnosed patients per year totals over $1 billion in total costs.

Our review of PA promotional literature indicated that site of service was also part of the PA vendor’s service line, but we have never seen or heard of this in fact. This is surprising because we estimate that the site of service excess costs for self-funded employers is at least $6 billion, making the total addressable market (TAM) for potential savings around $10 billion. This $10 billion opportunity cost from ignoring site of service savings is very real, and not addressed in medical oncology PA.

Even if the PA process saves 10%, gross 5% net of oncology drug cost for the 50% of the market served by private payers, and assuming that the total market for oncology drugs is 55-65 billion, the total potential savings is from PA is $2.75-3.25 billion.

So, the cost/benefit equation for PA is ($Billions):

$3.0 avg. total savings – $1.0 provider cost -$1 delay costs – $10 site of service = ($9.0 system cost)

Diagnosis & Care Planning Analysis – Value Stream Mapping Considerations

  • The value of DCP: Proper diagnosis and care planning is the essential first step in treating cancer patients. In medical oncology, advances in diagnosis coupled with the concurrent growth in precision medicine has been instrumental in extending survival for cancer patients in the US. DCP is the essence of value creation in the process.
  • Time and cost considerations of value creation: Drugs are the mechanism for value creation, and in a world of targeted therapy, getting the right combinations of drugs to the patient safely and on a timely basis is the entire mission. Feedback is critical during the process, and changes may be required based on patient/drug interaction. This is always unique to the individual.
  • Time and cost considerations of waste and excess cost in DCP delivery: the DCP process is fraught with delays in testing and diagnostic variability. We have identified PA as a process that contributes additional administrative burden and direct patient costs, which we estimate at $2 billion in total. This does not include the cost of degradation of patient care which will require additional study to estimate.
  • Opportunity Cost – Financial: PA does not address the area of greatest cost disparity – site of service. It is critical to note that nothing in our system requires that drug infusion be tied to the site of the physician responsible for DCP, anymore than imaging needs to be tied to the site of the requesting physician. We estimate that there is $10 billion in site of service potential savings – exceeding the total estimated maximum savings from the current PA system by 330%.
  • Opportunity cost – racial and socio-economic: Disparities in testing and availability of precision medicine have long been associated with medical oncology. In the Hawaiian case referenced above we are starting to see a very real financial cost being imposed on pharmaceutical companies for inadequate testing related to drug use. As demonstrated in Figure 8 below, the detail of genetic data for drug targeting will increasingly drive care planning decision with the need to recognize, understand and respect the need for different therapeutic choices.

Figure 10

  • Incompatibility with new technologies: One of the main complaints about PA is that all the systems are idiosyncratic and largely incompatible with each other and most EMRs. This requires a team of employees focused on the PA systems for each provider group.

Go ahead, Friend, find the waste in prior authorization!

Figure 11

  • Summary and Conclusions: The DCP process is evolving very rapidly, driven by new drugs and genetic test identifying molecular targets for new precision cancer therapies. Most of these new targeted drugs address small numbers of patients and are classified as orphan status by the FDA. Because the markets are small, the difficulty in patient identification depends on specialized NGS tests, which are expensive and not universally approved for reimbursement. The PA process for both the drugs and the tests to identify their use are subject to PA requirements, which are idiosyncratic in their design and application. There is limited evidence of the efficacy of PA as an effective method of either cost control of safety. We are not aware of any PA system which addresses the largest source of drug spending waste in the system – site of care pricing differentials. An ASCO team addressed the disparities in the equity of care delivery in an editorial in JCO this April (here), “Advances in systemic and targeted cancer therapies have not benefited all patients equally. Cancer care disparities, …are established before the point of care, carry forward to unequal access, delayed detection and diagnosis, and underuse of therapy or receipt of suboptimal therapy, leading to poorer health outcomes.” In aggregate, we estimate that the system has a net cost burden of at least $9 billion per year and materially degrades the quality of care based on physician surveys focused on delays and harm.



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Wes Chapman
Written by Wes Chapman

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