Personalized medicine in neurology

Potential of digital health technology to complement care

A need for speed, as time is brain

It is estimated that 1 in 3 people will suffer from a neurological disorder at some point in their lives. In addition, because of the growing and aging population, the number of people diagnosed with brain disorders is increasing fast. Furthermore, though still early to evaluate the exact impact, it is expected that the number of people with brain disorders will only grow further because of the COVID-19 pandemic, as 1 in 3 of COVID-19 survivors has a neurological or psychiatric condition (Mahase 2021).?

Though the saying ‘time is brain’ was originally used for patients with acute ischemic stroke, it holds for all brain disorders, including chronic disorders such as Multiple Sclerosis, Parkinson’s disease, epilepsy, etc. In contrast to the fields of oncology and diabetes, in which genomics and blood glucose measures, respectively, are used for personalized decision-making and treatment optimization, neurology care hasn’t entered the era of personalized medicine yet. Despite the immense need, and the fact that an increasing amount of disease-modifying therapies are available for brain disorders.?

Identifying the need to initiate or escalate a patient’s treatment gives rise to the complex exercise of matching patients with the most suitable drug available. The transformation of conventional disease-centered caregiving (based on broad disease categories) towards personalized medicine (based on individual characteristics, disease subtype, risk, prognosis, or treatment response) will help meet the unique needs of patients.?

In order to make more personalized and data-driven decisions, reliable and clinically validated information needs to be added at the different levels of the patient care path. The source of such data can be medical imaging, health records, health claims, data from wearable sensors, and mHealth apps, next to genomics, metabolomics, etc.? Such data-driven insights require the organization and analysis of massive datasets to identify features that may indicate optimal treatment decisions. Hence, AI solutions play a crucial role, by integrating multimodal data to generate actionable insights. The resulting predictive models can aid in improved patient stratification, prediction of progression, and therapeutic response (Fr?hlich et al. 2018).

Personalized medicine has the advantage of reducing health costs by improving medication effectiveness, reducing adverse risks and side-effects by preventing unnecessary therapies, optimizing the use of therapies, and improving disease management by means of wearable technology and mHealth apps. Next to this, personalized medicine can contribute to the smarter design of clinical trials by selecting likely responders at baseline (Mathur et al. 2017, Schork et al. 2019).

But it all starts by implementing a care path that is data-driven, flexible, efficient, and patient-centric. One that combines the clinical expertise of neurologists, nurses, radiologists with the personal insights of patients and caregivers, and the technology that allows to translate all the information into actionable insights and to integrate these into the healthcare systems.

Digital health technology, easier said than done

The COVID-19 pandemic has demonstrated that digital health tools are becoming standard of care rapidly, as they improve workflow efficiency and complement clinical expertise. Remote patient monitoring through medical health (mHealth) applications allows for continuous and more objective monitoring of symptoms and clinical disease activity. In addition, regular standardized check-ins through validated mHealth apps can mitigate the underreporting of patient-reported outcomes and bridge the information gap between annual neurology visits.?

Similar to the use of artificial intelligence (AI) in healthcare, a mHealth solution is never the goal in itself, but a means to an end, a way to improve efficiency, a piece in the complex puzzle to improve outcomes of patients. In this context, in order to have an impact on clinical decision making and patient outcomes, it is crucial that the digital health solutions are:

1. broadly validated in a real-world setting: as the goal of these digital health tools is to impact the lives of patients in a real-world setting, they need to be validated in this real-world setting. Because of the variability in real-world care, caused by country regulations, personal preferences, hospital workflows, etc., thorough validations and clinical studies are needed in this setting to make sure that the solutions can deal with the existing level of variability.?
2. cleared for clinical use as a medical device: in order to impact clinical decision-making and patient outcomes, medical device approval is necessary. In this context, digital health solutions need to be CE-marked in Europe and FDA-cleared in the US as medical devices.
3. integrated seamlessly into hospital systems: all physicians are overworked and burnout rates are very high (up to 45%), hence it is crucial that digital health solutions not only improve the care of patients but also positively impact radiological and clinical workflows. To this end, seamless integration of digital health tools in hospital systems is essential.?
4. complying with any local data security regulation: in the last decades, data privacy and security measures have become crucial in healthcare. In this context, digital health solutions need to comply with global and country-dependent regulations and digital health companies need to invest significantly in certifications such as ISO:27001, beyond their core technology development.
5. providing clear value to all stakeholders: in addition to providing value by improving care and clinical workflows, digital health solutions need to positively impact all stakeholders, but mostly the patient. The patient indeed plays a central role in this new healthcare system and patient awareness, engagement, and education are essential.??

Clinical expertise and digital health technology, a match made in Leuven

The global and sustainable implementation of personalized medicine will depend on data-driven, integrated, validated, and scalable solutions. The transition from traditional to personalized care will be gradual and will evolve together with the development and adoption of software-assisted decision support tools, wearable technology, mHealth apps, and digital therapeutics. The key will be to integrate these solutions and capture, organize, analyze and interpret the resulting data to generate meaningful clinical insights.

In our recent paper, we presented a novel digital care management platform to monitor clinical and subclinical disease activity, as it is applied in Multiple Sclerosis (Van Hecke et al. 2021). The aim of this platform is to provide the MS care team with clinically relevant data that help in clinical and treatment decision-making. In the figure below, an overview of the CE-marked and FDA-cleared digital health solutions is shown.

In this manuscript, the real-world impact of the care platform on patients, radiologists, and neurologists was evaluated, demonstrating enhanced hospital workflows, a significantly higher sensitivity to detect clinical and subclinical disease activity, and important patient empowerment.?

?References

• Fr?hlich H,? et al. (2018). From hype to reality: data science enabling personalized medicine. BMC Med 16, 150.?
• Mahase E (2021) Covid-19: One in three has neurological or psychiatric condition diagnosed after covid infection, study finds, BMJ 2021; 373
• Mathur S, Sutton J. (2017). Personalized medicine could transform healthcare. Biomed Rep. 7(1):3-5.?
• Schork NJ. (2019). Artificial Intelligence and Personalized Medicine. Cancer Treat Res. 178:265-283.?
• Van Hecke W et al. (2021) A Novel Digital Care Management Platform to Monitor Clinical and Subclinical Disease Activity in Multiple Sclerosis, Brain Sciences 11, 1171.