Guest Column | May 23, 2019

Establishing Data Models To Support Cell And Gene Therapy Adoption

By Rob Innes, Wyoming Interactive


Cell and gene therapy offers extended-term relief from disease states but comes at high cost with a complex reimbursement model. What proportion of intervention costs should be levied up front, and what proportion can be phased into the future (when the patient benefits accrue and are proven)? Separating the payment timeline from the treatment schedule is often referred to as outcome-based costing.

Data to support this calculation comes in many formats from many sources. If relevant data sources can be effectively identified and managed, they can provide valuable insights to cell and gene therapy decision makers — from manufacturers to payers to care providers.

Advanced Therapies: A Rapidly Growing Sector

Transformative medical interventions to cure complex disease states using cell and gene therapy sound like wonder drugs for those affected: disease-free living with no long-term care worries. But, can the promise be delivered, and how quickly will these get into the hands of clinicians? The first wave of therapies from pharmaceutical manufacturers such as Spark Therapeutics, Novartis, and Gilead/Kite Pharma is now becoming available and likely will be joined by many more in the near future.

The number of preclinical stage compounds rises every year, and the consequent number of trials is increasing rapidly. The FDA reported on Jan. 15, 2019, that it has around 800 investigational new drug applications for cell and gene therapy and expects to add 200 new trial applications per annum from 2020. These applications will take time to commercialize (and many will not make it to commercial use), but the FDA is anticipating between 10 and 20 cell and gene therapies to be approved each year, starting in 2025.1

With such strong activity in the sector, it is highly likely that expedited regulatory pathways will be deployed, especially for disease states that currently have poor treatment options for patients. That should incentivize development and potentially accelerate deployment. However, the treatments are expensive. Cell and gene therapy treatments could reach $500,000 per patient, and most patient funding plans were not designed with these therapies in mind.

If a patient with a severe disease state is offered a treatment promising 15 years of disease-free living, there are few who would not want the intervention. Patient desire, however, comes up against economics — the following are some of the obvious challenges with these kinds of interventions:

  • Cost of therapy challenges payer organizations with very large up-front commitments. 
  • Long-term healthcare benefits are unproven — until a patient has lived disease-free for 15 years, there is no data to show a 15-year promise was realistic.
  • Manufacturers want a secure revenue source to encourage research and to reimburse them for an expensive product (and to fund future research and development).

If up-front payment is challenging for payers, an alternative, outcome-based option is required.

New Reimbursement Models Are Needed

First, a definition … what is a reimbursement model?

It’s a payment mechanism that releases agreed-upon funds from a payer to a manufacturer once certain metrics are reached. The model may have brakes and accelerators added to slow down (or even stop) payments or to bring them forward based on real-world outcomes being achieved.

It’s like multi-stage escrow; contractually agreed-upon payments are made once agreed-upon conditions are met. Using data from many sources, the model gives payers confidence to release funds as if treating a chronic condition with ongoing payments. But instead of confirming the chronic condition persists (and paying for its managed relief), with cell and gene therapy, the payments are made for continued relief from original symptoms.

A new reimbursement model could benefit outcome-based costing because it:

  • gets the medical intervention underway for the patient without funding holdups
  • fairly rewards transformative interventions for manufacturers
  • creates a brake mechanism for payments when treatment fails to deliver on promises, protecting payers
  • provides confidence to fund future research and encourages other manufacturers to enter the field with novel methods and solutions.

An effective reimbursement model will be driven by data — data about the patient and their detailed health status, collected over time, consistently and, critically, electronically. This raises questions over electronic health records or imposes a data transcription burden somewhere in the collection phase. For without data, there can be no reimbursement model. Participants in cell and gene therapy, including specialist treatments centers and primary care clinics, must commit to the data standard and the collection burden for the long term. This could present a roadblock. (For more on this topic, see my recent article "Data Collection Challenges in Healthcare.")

Assuming longitudinal data collection is in place, an electronic form is available, and permission is granted to share with model participants, then there is scope to model the health of the patient relative to the therapy. Does the patient exhibit good health measured, perhaps, via some variation of quality-adjusted life years? Are there secondary health issues that interfere with the original therapy or limit its effect? Can data from individual patients be aggregated across cohorts to form trends for each intake and predict outcomes for new patients? Without good data, there can be none of these.

Challenges With Reimbursement Model Data

Reimbursement model data presents a multitude of challenges and unanswered questions to both healthcare providers and patients. From geographical to validation concerns, Wyoming Interactive has identified a few key considerations:

  • Healthcare facilities need to standardize information collection and share data via standardized protocols, creating a task burden that may not be currently resourced.
  • Information sources may have to extend data collection points and possibly change frequency and format to support downstream (reimbursement model) needs.
  • Ultimate use in a reimbursement model is not the prime focus at the point of collection (patient engagement), so there may be resistance or operational restrictions to overcome.
  • Data collectors are clinical team members not rewarded by long-term health data success, so the validity (or otherwise) is not a collection priority.
  • Patients may relocate out of the coverage area or between payers with different systems and SOPs.
  • Patients may not commit to long-term monitoring, especially if their symptoms have been dramatically reduced or even ceased; adherence may require incentivization.
  • Clearly establishing negative health outcomes experienced by patients which are or are not related to the original diagnosis/treatment plan could be a challenge.
  • Tracking adverse events and relating them to treatment protocols will be necessary.
  • Who arbitrates in the event of a dispute?
  • How can collected data be validated?
  • What commitments must patients agree to over the long term to gain access to intervention?

A reimbursement model will require information from clinical settings, and there will already be collection processes running. Collection may match the reimbursement model requirements. However, this is likely to be accidental rather than by design, and, in many cases, a reimbursement model will require additional data collection effort and the burden will fall on resources not focused on the later data use. That is, existing clinical roles will be expected to absorb the additional load without consideration of their clinical responsibilities. Clearly that is not a feasible approach for widespread adoption, and collection, transcription, and management of clinician-sourced extended data streams is a critical enabling step for reimbursement models to take off.

There are strategies to work around this dilemma, such as funding a role to support information capture in some trial locations. A case would need to be made to add this role and its costs into the provisioning authority, backed up by a funding plan via the reimbursement model. In a period of transition, flexibility and funding support will be critical.

Permission granting and security are naturally areas of uncertainty and pose tricky questions which, at this early stage, are difficult to answer:

  • Who owns the data? Is patient data actually the patient’s data, particularly with the extent and personal nature of the data set?
  • To what extent does permission need to be obtained when additional consumers of the information, separate from traditional care providers, need access to the data?
  • Can rich patient data be mined for other purposes? Even if the side use has beneficial effects, either directly for the patient or for the community at large, can this be justified?


Outcome-based costing in some form will be part of healthcare provision for advanced therapies, and likely sooner than many may have expected. It’s clear that if a data model can be established, a shared risk/shared reward approach could accelerate cell and gene therapy adoption and transform access for cautious payers, creating, quite literally, life-changing outcomes for patients.



About The Author:

Rob Innes is head of consultancy at Wyoming Interactive. In this role, he manages consulting engagements for the firm’s life sciences clients, helping to drive business transformation in response to industry challenges.