Top pain points in the delivery of mental healthcare and how digital technology can help

Top pain points in the delivery of mental healthcare and how digital technology can help

Close to 1 in 7 people suffer from a mental health condition [1], a leading cause of disability globally [2].
However, 75% of people with mental illnesses do not receive any treatment [3], highlighting the issue of access to mental healthcare, which has only been exacerbated by Covid.

The shortage of trained professionals in mental healthcare is a key contributor to the issue of access [4]. Patients are faced with long waiting queues to receive care [5], with no promise of quality care, given clinicians’ heavy caseload [4]. It is not uncommon that patients only receive 5 mins of clinicians’ time after months of waiting for the appointment [6]. Indeed, a large study by Elsevier Health (2022), involving over 2800 clinicians and nurses from 111 markets, found that almost 1 in 2 of clinicians globally (69% in Europe) admit that time they are able to devote to each individual patient is insufficient “to give them good care” [4].

Clearly, there are plenty of opportunities to improve patients’ access, speed to, and quality of care globally. Promisingly, the same study found that over half of the clinicians (56%) state that patients have become more empowered to manage their own conditions, and that clinicians (62%) expect a change in role towards being more in partnership with patients over the next decade. Given that mental health costs a whopping $16 trillion to the global economy by 2030 [7] and growing, there is an urgent need for solutions that are designed to tackle these issues in a scalable and cost-effective way.

The use of digital technology offers the potential to address this matter. In particular, the use of digital platforms for remote patient monitoring and health assessment could improve access and speed to care, and real-time patient analytics could enable personalised treatment and improved quality of care [4]. Ultimately, to fully benefit from such technology, patient data needs to be managed securely, the design of the solution should focus on the needs of its users, and it should be continually assessed on its ability to deliver value to patients and clinicians.

About Monsenso:
Monsenso is an innovative technology company offering a digital health solution used for decentralised trials, remote patient monitoring and treatment support. Our mission is to contribute to improved health for more people at lower costs by supporting treatment digitally and leveraging patient-reported outcomes data. Our solution helps optimise the treatment and gives a detailed overview of an individual’s health through the collection of outcome, adherence, and behavioural data. It connects individuals, carers, and health care providers to enable personalised treatment, remote care, and early intervention. We collaborate with health and social care, pharmaceuticals, and leading researcher worldwide in our endeavours to deliver solutions that fit into the life of patients and health care professionals. To learn more visit  www.monsenso.com.

References:
[1] World Health Organization (2020). World Mental Health Day: an opportunity to kick-start a massive scale-up in investment in mental health.
https://www.who.int/news/item/27-08-2020-world-mental-health-day-an-opportunity-to-kick-start-a-massive-scale-up-in-investment-in-mental-health#:~:text=Mental%20health%20is%20one%20of,every%2040%20seconds%20by%20suicide.

[2] Wainberg, M. L., Scorza, P., Shultz, J. M., Helpman, L., Mootz, J. J., Johnson, K. A., Neria, Y., Bradford, J. E., Oquendo, M. A., & Arbuckle, M. R. (2017). Challenges and Opportunities in Global Mental Health: a Research-to-Practice Perspective. Current psychiatry reports 19(5): 28. https://doi.org/10.1007/s11920-017-0780-z.

[3] Marchildon, J. (2020). 4 Barriers to Accessing Mental Health Services Around the World.
https://www.globalcitizen.org/en/content/barriers-to-mental-health-around-the-world/.

[4] Elsevier Health (2022). Clinician of the Future Report 2022.
https://www.elsevier.com/connect/clinician-of-the-future.

[5] Royal College of Psychiatrist (2020). Two-fifths of patients waiting for mental health treatment forced to resort to emergency or crisis services.
https://www.rcpsych.ac.uk/news-and-features/latest-news/detail/2020/10/06/two-fifths-of-patients-waiting-for-mental-health-treatment-forced-to-resort-to-emergency-or-crisis-services.

[6]. Becker, G., Kempf, D.E., Xander, C.J. et al. (2010). Four minutes for a patient, twenty seconds for a relative – an observational study at a university hospital. BMC Health Serv Res 10(94).
https://bmchealthservres.biomedcentral.com/articles/10.1186/1472-6963-10-94.

[7] Lancet Commission. (2018). Report: Mental illness will cost the world $16 trillion (USD) by 2030. Mental Health Weekly 28(39): 1–8. https://doi.org/10.1002/mhw.31630.

New research: Can smartphone data be a digital marker for discriminating bipolar disorder from unipolar disorder?

New research: Can smartphone data be a digital marker for discriminating bipolar disorder from unipolar disorder?

Unipolar disorder (UD) refers to individuals suffering from depression without experiencing mania, whereas individuals suffering from bipolar disorder (BD) usually face episodes of mania in addition to their depression [1]. Clinicians often encounter difficulties identifying whether depressed patients suffer from BD or UD. Given that the course of illness and related treatments vary for patients with BD and UD, the discrimination between these two disorders is critical [2].

A new research paper has just been published on “Differences in mobility patterns according to machine learning models in patients with bipolar disorder and patients with unipolar disorder” in the Journal of Affective Disorders [2].

This new research investigated whether using the information on activity and mobility of patients with BD and UD as supplementary objective measure could assist in the discrimination between the two conditions [2]. Data for this study has been collected as part of the RADMIS trials, two similarly composed randomized controlled trials (RCTs) that investigated the effect of daily smartphone-based monitoring including a clinical feedback loop in individuals suffering from BD and UD. The Monsenso digital health solution was used for the collection of smartphone-based patient data in the intervention group of the trials [3].

The present study included gathering both passively collected smartphone-based location data and patient-reported smartphone-based data on mood from 65 patients with BD and 75 patients with UD [2]. Smartphone-based self-assessments of mood were completed by all patients, and smartphone data on location reflecting mobility patterns, routine and location entropy (chaos) was collected passively from all patients on a continuous basis over the course of six months [2]. The data collection was followed by an extensive data analysis, comparing differences between the two groups.

Results of the study show patients suffering from BD have significantly lower mobility in, e.g., their total time of daily movement during depressive periods (eB 0.74, 95% CI 0.57; 0.97, p = 0.027). Additionally, the area under the curve (AUC) of location data was rather high in classifying patients with BD compared with patients with UD, although results of the study may be limited by relatively low symptom severity of the participating patients contributing to the dimension of the AUC [2].

The study results suggest alterations in location data may be a promising digital diagnostic marker in patients with BD and UD, and smartphone data on mobility patterns could hence help in discriminating between the two disorders. 

Mads Frost, PhD, Co-Founder & Chief Information Security Officer at Monsenso, who has contributed to the research says: “The work on comparing mobility patterns between patients with bipolar disorder and patients with unipolar disorder has been highly interesting, and we look forward to further explore our data looking for potential digital diagnostic markers”.

 “We are excited that Monsenso is a part of promising new research on digital diagnostic markers, and contributes to the research in and the treatment of mental health and neurological disorders”, says Thomas Lethenborg, CEO at Monsenso.

About Monsenso:
Monsenso is an innovative technology company offering a medical grade digital health solution. Our mission is to help provide better mental health to more people at lower costs. Our solution helps optimise the treatment of mental disorders and gives a detailed overview of an individual’s mental health through the collection of outcome, adherence, and behavioural data. It connects individuals, carers, and health care providers to enable personalised treatment, remote care, and early intervention. Based on continuous research and development, our team is committed to developing solutions that fit seamlessly into the lives of individuals, increase their quality of life and improve the efficacy of mental health treatment. To learn more, visit www.monsenso.com

Research publication:
You can find the research publication in the Journal of Affective disorders here.

References:
[1] Quilty, L., Pelletier, M., DeYoung, C.G. & Bagby, M. (2013). Hierarchical personality traits and the distinction between unipolar and bipolar disorders. Journal of Affective Disorders 147(1-3): 247-254. https://www.sciencedirect.com/science/article/pii/S0165032712007604#bib2

[2] Faurholt-Jepsen, M., Busk, J., Rohani D.A., Frost, M. Tønning, M.L., Bardram, J.E. & Kessing, L.V. (2022). Differences in mobility patterns according to machine learning models in patients with bipolar disorder and patients with unipolar disorder. Journal of Affective Disorders 306: 246-253. https://www.sciencedirect.com/science/article/pii/S0165032722003019?dgcid=author

[3] Faurholt-Jepsen, M., Tønning, M.L., Frost, M., Martiny, K., Tuxen, N., Rosenberg, N., Busk, J., Winther, O., Thaysen-Petersen, D., Aamund, K.A. & Tolderlund, L., Bardram, J.E. & Kessing, L.V. (2020). Reducing the rate of psychiatric re-admissions in bipolar disorder using smartphones—The RADMIS trial. Acta Psychiatrica Scandinavia, 143(5): 453-465. https://onlinelibrary.wiley.com/doi/10.1111/acps.13274