Patients increasingly act like consumers, expecting to control decisions about their care and to receive individualised products and services. The rise of the patient–consumer, in combination with reimbursement and regulatory pressures, presents new opportunities for personalised medicines and devices to command premium market positions.
In this series, we have previously explored a gamut of options for developing precision medicines, devices, and diagnostics - from emerging biomarker strategies to patient preference information.
In this instalment, we will survey the potential roles for technology - including wearable devices, mobile apps, and big data analytics platforms -in the development of novel precision medicines that engage the patient–consumer. In particular, technology offers developers unique opportunities to stratify patients and deliver more personalised care by at least five strategies.
1. Mitigate the risk of non-adherence diluting an intervention’s effect
Adherence is a major challenge for many indications; non-adherence can reduce the net efficacy of an intervention in target demographics. Wearables and companion apps can be used to identify patients who need the most guidance and engage personal networks to encourage behavioural change. This category of wearables and apps provides safeguards against non-adherence, thereby enhancing the efficacy of a doctor's treatment.
Depending on the indication, there are two monitoring and support mechanisms to explore: apps that provide patients with continuous reminders based on either a) a doctor's orders or b) a patient’s own physiology.
In the first case, an app called Medisafe lets patients and their families track prescriptions and their own adherence to those prescriptions. Medisafe can illustrate the barriers to that patient's adherence, and the app's developer claims that Medisafe increases adherence to prescriptions by 72%.
A second mechanism to encourage adherence is employed by Dexcom's Continuous Glucose Monitor, which continuously alerts diabetics and their families of adverse changes in the patient’s blood glucose level. This app allows family members to assist the patient even when the patient is unable to resolve an adverse event on their own.
The latter category of apps enables a patient to engage with their own disease management in ways they could not before, which may in turn, increase adherence. For instance, technologies that allow hypertension patients to self-monitor their blood pressure have been available for some time. These devices are known to increase adherence to hypertension medication, possibly because patients are motivated by seeing themselves achieve target levels.
While mobile technologies do show considerable promise, a 2013 analysis of smartphone apps by researchers at the University of Arkansas indicates that the efficacy of adherence apps may not have been thoroughly tested. Developers should be cognisant of the adherence issues for the wearable device or app itself.
For example, could a non-compliant patient who received a step tracker to encourage physical activity choose to mislead physicians by strapping the device to his or her dog? Non-adherence has also been observed in a substantial segment of fitness wearable owners: one-third of them reportedly stop using the devices within six months. Are there certain patient populations or user interfaces that lend themselves to reduced long-term adherence? Can social feedback mechanisms or new user-relevant features be integrated to encourage longer-term use? Can the patient’s use of the device be verified?
Before introducing a new technology in a clinical setting, either alone or in combination with an existing intervention, it is necessary to validate the apps or wearables for the intended clinical use and patient demographic.
2. Deliver treatment at the right time, even for unpredictable episodic indications
Precision medicine is about ensuring that patients receive the right intervention at the right time. However, some indications, by their nature, require episodic treatment on an irregular, unpredictable basis. Often, proactively treating those episodes can reduce their severity. Thus, a system that helps patients predict episodes can, from the patient's perspective, deliver on the promises and benefits of precision medicine.
Some apps target the issue of unpredictability by assessing patterns in a patient's environment and interpreting them for the benefit of the patient. For instance, the Icahn School of Medicine's Asthma Health app not only tracks a patient's symptoms and treatments, but also alerts him or her about local asthma triggers and sends these data to the patient's doctor so he or she can adjust the patient’s medications if needed.
This app makes it easier for patients to engage in proactive care and could potentially serve as an adjuvant to episodic treatment plans
3. Accelerate intervention
Some wearables let patients directly collect data on their own health and instantly transmit that information to their doctor, allowing for more immediate care from healthcare providers. This strategy can turn, for example, an otherwise standard EKG into a personalised direct line for earlier alert and treatment.
One innovative wearable called Kardia, by AliveCor, turns a patient's smartwatch into an EKG that can detect atrial fibrillation, the most common heart defect. Data for an event can be immediately transmitted to a doctor, along with a voice note about how the patient is feeling. This wearable produces a more efficient means for diagnosis and treatment, and can augment a patient's self-care regimen.
4. Reduce risk during the inpatient-to-outpatient transition
For indications impacted by shifts to bundled payment or episodic care reimbursement models, technology can augment the economic value proposition of a product. Many apps now target the critical period immediately following hospital release and function to minimise readmission and non-reimbursable adverse events.
One of those apps, Care at Hand, helps elderly patients regularly update their providers about their health and filters patient responses to alert front-line healthcare staff when patients need care. After adding this app to their standard care regimen, one elder care centre reduced its 30-day hospital readmission rate by 40%. A women's hospital trialled a similar app for care after breast-reduction surgeries. This app helped reduce the need for follow-up visits, lowered the 30-day readmission rate, and suggested post-op costs could be cut hospital-wide by up to 30%.
The successes of these two apps demonstrate the extent to which a product's reimbursement potential can be enhanced by the personalised provision of care at critical transition points.
5. Cost-efficiently deliver mass-personalised care
Apps can also add value to an individual's treatment regimen by delivering personalised advice to live a healthier life without overtaxing a physician. MD Revolution provides patients with personalised care from healthcare professionals and, at the same time, automates the process to reduce the burden on physicians and other front-line staff.
MD Revolution's RevUp app promotes healthier lifestyles in patients with chronic conditions by letting them track their own activity, food intake, and exercise. Personalised care teams composed of nurses, dieticians, and exercise physiologists actively monitor these data and send patients instant messages based in behaviour change psychology. This app helps reduce the care team's need to write every message de novo by segmenting audiences into personalised care pathways. This enables personalised care on an approximately $40 per month Medicare Chronic Care Management reimbursement.
Developers of medical apps and wearables would be wise to monitor the rising concerns about the privacy of personal data in mobile devices, apps, and cloud services. Potential exists for these concerns to escalate and influence a product’s future launch environment.
Medical apps and devices record highly personal information, possibly in greater volumes than many other apps on the average smartphone. The breadth and depth of this information may soon rival a patient's health records and may even include interactions that patients could be too reluctant to have with their doctor. This longitudinal record of a patient’s (near) natural course of behaviour is unprecedented for drug and device developers.
Consequently, future consumers may have heightened concerns about how the technology will protect all of those data when a device is stolen or an account is hacked. Apps may also unexpectedly record events that go beyond the consumer’s originally conceived expectations for disclosure, such as a signature of a cardiac condition in heart rate data recording for fitness coaching. Looking forward, these concerns may raise challenges — or opportunities — for product design and adoption.
As patients’ role in healthcare decision-making and their choices in personalised products expand, so will their expectations for clinical trials. Patient preference information, as we have discussed, can favourably enhance trial protocols and risk profiles.
Great opportunities exist to personalise the trial experience through innovative technologies, particularly those that efficiently connect patients to trials they desire, better educate prospective subjects on trial requirements to reduce attrition rates and increase trust, and protect patients from risks or errors that may otherwise go undetected.
Wearables and apps are, of course, one strategy to help reduce site visits, and thereby help patients better fit a trial into their lives. ICON is innovating a number of other data-driven technologies that operationalise the concept of patient centric trials to boost enrolment and promote compliance.
Recently, ICON and IBM partnered for an ongoing pilot program to transform patient recruitment using high-throughput cognitive computing. Recruitment is particularly challenging in the oncology space, as few patients report awareness of relevant trials (as few as 16% in one recent survey) and a minority of oncologists report systematic methods for staying current on actively recruiting trials.
In the pilot program, ICON and IBM are employing IBM Watson Clinical Trial Matching to directly recruit cancer patients through electronic medical records (EMRs). Watson, IBM’s cognitive computing engine, reads physicians’ notes in major medical centres’ EMRs to identify patients who both match eligibility criteria and are connected to participating investigators.
Watson then notifies investigators of trials relevant to their patients, facilitating direct recruitment of any potential participants without more resource-intensive approaches such as advertising. Additionally, from a patient–consumer’s perspective, the direct-through-EMR strategy will enhance the quality of service available from his or her trusted physician.
The IBM Watson Clinical Trial Matching program also enables ICON to conduct real-time feasibility analyses and model hypothetical protocol scenarios. Using EMR data on treatment history and pre-existing conditions, ICON could not only assess a potential patient population for their eligibility for a particular trial, but also determine where those patients are located and how to recruit them.
Another focus area for ICON’s efforts to streamline trials and engage patients in their own care is the informed consent process. Informed consent, despite intense monitoring, remains the source of error for 5% of all FDA findings. Furthermore, patients often struggle to fully comprehend long, technical informed consent forms before committing to a trial and its required schedule of visits and tests.
To help improve a patient’s comprehension of consent materials and ensure he or she receives “ample time” to review that information in accordance with ICH E6 guidance, ICON’s FIRECREST eConsent and Patient Portal technologies deliver multimedia educational tools through mobile devices and web-based portals that can be accessed both before and after a face-to-face meeting with a doctor. In the FIRECREST Patient Portal, patients can read visit-by-visit guides, provide feedback, and review updates or personal notifications. Investigators and sponsors can see a record of the materials patients view and forms they sign.
The platform thus pairs a typical patient’s expectations for audio/video content and self-paced educational materials with the regulatory requirements of clinical trials. In doing so, it markedly improves patient comprehension, recruitment rates, retention, and relationships with investigators.
Two processes that are less visible to patients than recruitment or consent, but no less beneficial in terms of placing value on a patient’s participation in a clinical trial, are real-time informatics and enhanced monitoring.
The ICONIK Informatics Hub is ICON’s technology platform for analysing the operational, clinical and real world data collected during clinical development. Its real-time data analytics and visualisations can enhance patient safety, for example, by enabling faster reporting of adverse events.
The ICONIK Informatics Hub also includes an adaptive risk-based monitoring strategy, called Patient Centric Monitoring (PCM), that can better detect errors that endanger patient safety. PCM employs human factors classification — a root cause analysis approach also adopted by NASA, Ford, and major airlines and militaries — to systematically identify the underlying behaviours and factors that would otherwise be difficult for even experienced monitors to sense or reconstruct, but ultimately are the root cause of incidents that affect patient safety in a trial.
Data from PCM empowers CRAs to deploy bespoke risk-mitigation strategies to sites that need them most, providing patients with the safest and most operationally standardised trial possible.
Looking ahead to convergence
Each of the development strategies discussed in this series, from biomarkers and biosensors to patient preference information and wearable technologies, present a gamut of opportunities for new drugs and devices targeted to specific patient populations. Notably, many of the aforementioned approaches and technologies will work synergistically to create greater value, both in the clinic and during development.
As data accumulates on these strategies’ utility on an individual basis, we should expect to see rapid convergence of multiple strategies, generating ecosystem-based pipelines of drugs, devices, sensors, diagnostics, and apps that guide patients from one treatment to another. The future of the drug and device industries will likely be more highly interconnected than it is today, driven together by the patient, payer, and regulatory forces that are currently shaping personalised medicine.
This blog is part three of our ‘Precision Medicine for the Patient-Consumer’ series: