Maximize Value Through Process Transformation
4. What Is Your System IQ?
Intelligent application of data, tools, agility and experience can help identify and close your value gaps. Is your intelligence giving you enough vision and clarity to learn from the past and prepare for the future?
Series 
2
Episode 
4
Published on
January 11, 2019
“I won't be impressed with science until I can download a waffle.” — Sean Gabay (1)

“How can a CRO (contract research organization), better serve its clients by accelerating enrolling qualified patients into their studies?”

‍Cut to the punchline: Harvesting the potential value of managing clinical trials requires accruing (enrolling) enough participants. Yet many trials fail, or are too slow, to accrue. Data-mining, automated engagement of candidate participants, facilitating tasks for the investigators, and real-time performance monitoring can accelerate accrual to launch your clinical trial on time and budget.


Welcome back to TeloChain’s Real-World Healthcare Insights! This is the fourth episode in a series on how secure technology-enabled process transformation and strategy redesign can help you harvest the full value of healthcare services.

Last episode, we looked at one way to close the gap between the desired and delivered value of a healthcare service—via re-imagining the processes needed to deliver the service enabled by a technology platform, favorably impacting both numerator (results) and denominator (cost) of the value equation. (2) Today we address gap-causing pain points by integrating new processes into an existing strategy and platform to enhance their effectiveness.


How’s your (system) IQ?

Systems (data, processes, metrics, feedback, people, devices) are informed and defined by their intelligence: Given the business, intelligence encompasses gathering and mining data to transform it into information that can inform strategy. Once executed, intelligence allows us to monitor progress, and—circling back—inform improvements to future strategy or its implementation.

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From the data, we extract what we need, informed by our purpose and goals - but it’s the usefulness (relevance and actionability) of information that transforms data to intelligence.

The process of gathering and applying intelligence to make decisions is complex and critical. Good intelligence is the culmination of a multitude of factors that, when applied in harmony, provide clarity and direction. Intelligence can determine what your goals should be and how to measure progress towards those goals. It can reveal risk so that you may protect yourself proactively. It can optimize your resource allocation, suggest areas of expansion, and allow you to target customers.


In healthcare, hundreds of millions of lives depend on gathering and applying the right intelligence. There are four key factors that comprise quality intelligence:

  1. Data: The first thing that likely comes to mind when we think of intelligence is the data that informs it. You need enough of the right kind of data, but in the real world, obtaining enough of the right data is far from easy. You must gain access to rich data sources, or when that isn’t possible, use the right tools to get as much as you can out of what you have.
  2. Tools: Any framework, technique, technology, or otherwise tangible or intangible thing that enables work is a tool. This includes hardware, software,  and analytical techniques.
  3. Agility: Good intelligence requires a balance between perfection now and perfection over time. You must strive for the best possible intelligence outcome in an appropriate time frame to act on it, but you can build your model to improve as more data is entered.
  4. Experience: Expert interdisciplinary perspectives add insights to further understand and interpret the data in ways that following procedures or applying off-the-shelf technology cannot. Experience adds a human touch to an otherwise computationally and procedurally heavy field.

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TeloChain has access to robust health data spanning 200 million lives. Our cloud infrastructure, custom platforms, and advanced analytics engines support processing the data to each project's needs. Our team spanning clinical, business, health economics, operations, and statistical expertise formulate accountable, self-improving action plans based on the best possible findings.


These key Intelligence factors are applied to the steps of gap-closure: Diagnose, Strategize, Redesign, Execute:

To close the value gap, identify pain points and diagnose their sources. Determine the extent to which the throughputs would benefit from a thorough redesign of processes versus focused enhancement of the current platform. This informs strategy: How to get from current to desired state, and execution. Apply the 4 components of Intelligence at each step of closing-the-gap

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Use case: Clinical trial accrual accelerator

Let’s demonstrate our gap-closing Diagnose → Strategize → Redesign → Execute approach using the Intelligence Data, Tools, Agility, Experience framework on a use case involving a contract research organization (CRO) managing clinical trials.

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Let's define some terms!

Screening: Identifying (1) patients who might be candidates for a clinical trial, based on their meeting basic criteria such as age, gender, diagnoses, and treatments; (2) physicians who treat patients like (1)

Qualifying: the process of matching a potential participant patient's demographic and clinical scenario to the trial's inclusion and exclusion criteria

Enrolling: the process of recruiting qualifying patients to a trial, informing them about the trial and the experience of participating, and gaining (and documenting) informed consent to participate. 'Enrolling' may also be applied to physicians who in turn recruit patients and/or become investigators in the trial

Trial patient participant: A person who has signed consent, been randomized or allocated to one of its cohorts, and has began or completed the activities specified in the protocol

Accrual: The number of participants enrolled

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Below, a simplified schematic of a clinical trial: In the pre-trial phase, research questions are developed, focusing on a study population defined by inclusion and exclusion criteria. (3) As part of the study protocol (recipe), minimum and target sample sizes are calculated—this is the number needed to accrue. In the accrual phase, potential patient participants are recruited by direct marketing or physician referral, and screened for eligibility. Those who meet the criteria are invited to participate. Upon signing the consent, they are allocated to one of the treatment cohorts, and enter the trial phase. For additional 101 on clinical trials, see our first series. (4)

Fundamental to the success of any clinical trial is accrual—recruiting enough participants to meet the Protocol’s’ criteria for ruling success in or out (5). A review of 2007-2010 registered oncology trials found that half of Phase 3 trials closed prematurely due to insufficient accrual. (6) A review of cardiovascular trials that started between 2006-2015 found 11% were terminated early, with 41% of those due to inadequate accrual. (7)

CROs—tasked with designing, enabling, monitoring, and reporting some or all of a trial’s phases and activities—have established policies and processes, data sources, and work flows—in short, a set of processes enabled by a platform. So why are studies failing to meet enrollment minimums?

To answer that question, we have to use all the data, tools, agility, and experience (intelligence) at our disposal to close the gap.

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Diagnose: Listening to the pain points our CRO is experiencing, we discover their platform as a whole is working well, but accrual is unacceptably slow. What’s needed is not a new platform, but accrual acceleration.

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Strategize: Using the data and tools available to us now, as well as the extensive experience of the study sponsor, CRO, and TeloChain teams, we identify that:

  • Casting a wider net would catch more potential qualifiers (8)
  • Engaging more physicians would enlarge the pool
  • Making it easy for physicians to refer patients would lower a barrier to accrual
  • Easing investigators’ work of collecting information and engaging potential participants in informed decision making would increase productivity
  • Secure online documentation of giving and retracting consent would ease logistical and compliance concerns


Redesign: Now comes the work of making a plan to address the problems based on the strategies identified.The Redesign phase involves adding in or modifying certain accrual processes and data-flows to work smoothly with the CRO’s existing platform.‍

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Execute: In this case, we identified that slow or inadequate accrual partially stemmed from an insufficient poolsize of potential participants (8). TeloChain’s access to 200M deidentified healthplan members’ medical/pharmacy claims enables casting a wider net to identify patient concentrations thus helping determine location priorities. A second pain point was the lack of time, training, and systematized approach to educating patients for our physicians. TeloChain addresses these concerns by providing needed data, hardware, and user interfaces to make it easy for physicians to initiate patient education and onboarding.

Additionally, using analytics, models and machine learning, TeloChain can identify potential participants based on criteria match, likelihood of enrollment and trial completion. This helps the CRO plan and execute the study more effectively and efficiently. By providing secure e-consent and secure document storage, TeloChain eliminates the time-wasting hassle factor of linking study documents. And attrition is prevented with engagement based on participants’ circumstances, health literacy, and preferences.

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Accelerating accrual with intelligence encompassed understanding the problems to be solved, selecting the right data to solve the problems, designing a workable and efficient strategy that fit within the CROs existing structure, and implementing the solution while closely monitoring its success.


Next up: Close the value gap—and do it securely.


Is your intelligence giving you enough vision and clarity to learn from the past and prepare for the future? Join the conversation below or contact us to see how we can create an intelligence strategy for you.

NOTES

  1. This quote bothers me for a number of reasons: First, it was very hard to find humorous quotes about research (at least ones that weren’t snarky). It’s easier to find humorous quotes about statistics and placebos (which pharmacies won’t fill without a fake prescription), but most of them didn’t seem to understand (and therefore shed light on, which is our passion in this blog) either statistics or placebos. Second, who is Sean Gabay? Google reveals several of them. Third, the quote is true only if Sean said it before the advent of 3D printing. Or was he referring to a virtual reality so robust that you couldn’t tell the difference between a so-called real and a downloaded waffle?
  2. Our example use case was a community lab service. The process transformation we described could increase the numerator by enabling the lab to expand its service to more patients—in other words, to grow. The denominator, cost, was reduced due to process efficiencies. Thus, value increased by delivering same results at lower cost, or by delivering more results at the same cost.
  3. For example, the ‘big’ research question might be “Does Newdrug prevent heart attacks (AMI—acute myocardial infarction) in diabetics who have had AMI despite adequate treatment with a statin?” That’s an important question, but too hard to study rigorously because taken literally the population would be extremely heterogeneous. We might focus the question as “Does Newdrug reduce the incidence of new AMI over a period of 3 years in patients with Type II diabetes age 40-80 who had AMI in the past year who were at least 80% adherent to a statin (dose equivalent to at least 40 mg/day of atorvastatin) for the period starting at least 12 months prior to their AMI through current and during 3 years of taking Newdrug?” These are our inclusion criteria; we might further refine the study population with exclusionary criteria by specifying that they may not have any of a list of medical conditions, or be taking certain drugs other than statin and Newdrug.  
  4. A problem not entirely solved by clinical trials is that their results don’t automatically translate into the real world. Partly this is due to trials’ strict inclusion and exclusion criteria (which account for just a subset of real-world patients); partly to patients in all comparison groups (including the control group) often receiving better care than they would in their non-trial setting; and partly to the hard-to-measure difference between people who volunteer for studies vs. those who don’t. Once a drug has been approved (because of its success in formal clinical trials), it is possible to at least partly ameliorate these concerns with pragmatic trials in a variety of clinical settings, loosening inclusion and exclusion criteria, and carefully-designed retrospective observational studies. We refer you to our first series for a deeper explanation of all this.  
  5. As you will no doubt recall from the statistics lessons you hopefully didn’t sleep through, it’s impossible to design an experiment that will prove beyond a shadow of a doubt that the intervention (treatment) worked. To do that, the data would have to allow us to conclude: (a) there is zero chance our experiment falsely concluded that the treatment worked when actually it didn’t (called a Type I error); and (b) there is zero chance that we failed to notice that the treatment worked, if it actually did work (called a Type II error). But we can get closer to being confident that our data will reveal whether our treatment worked by increasing the number of people in the treatment and comparison groups. We must first define ‘treatment worked’ by setting a threshold that must be equaled or exceeded, for example that a drug improved an outcome by 20%. Then we define how confident we want to be that the results of our sample of participants reflects what would happen to the entire population of people like those in our sample—if only we could test everyone. From this, we can calculate the minimum ‘sample size’ (number of participants) needed to be ‘confident’ (as we’ve defined it) that we didn’t miss a failure when our data said success (avoid Type I error), and that we didn’t miss a success when our data said failure (avoid Type II error). A typical set of criteria might read like this: “We will call the treatment a success if it improves the outcome by 20% compared to being assigned to not receive the treatment. We want less than a 5% probability of falsely concluding the treatment worked when actually it didn’t, and a less than 10% probability of falsely concluding the treatment did not work when actually it did.” The point here is that trials must accrue sufficient participants to meet these criteria.
  6. Mitchell AP, Hirsch BR, Abernethy AP. Lack of timely accrual information in oncology clinical trials: a cross-sectional analysis. Trials 2014;15:92.
  7. Baldi I, Lanera C, Birchialla P, Gregori D. Early termination of cardiovascular trials as a consequence of poor accrual: analysis of ClinicalTrials.gov 2006-2015. BMJ Open 2017;e013482. Doi:10.1136. The authors noted that early termination due to poor accrual declined slightly over the study period and that intercontinental trials fared somewhat better.
  8. “Potentially-qualifying” because the available data may not be sufficient to completely determine whether an individual is eligible for the study - a study investigator must make that determination.
  9. For insights on contents and use of payer datasets, see post in Series 1 episodes 3 and 4. These HIPAA-compliant data sets assign each individual healthplan member a unique masked identifier and contain basic demographic information (age range, gender, geography). Their medical and pharmacy claims provide dates, diagnoses and procedures for inpatient and outpatient services, including what labs and imaging was ordered (but generally not the results). Healthcare provider data includes specialty and may include membership in a group practice or healthcare delivery system.
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