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Central Nervous System

How do PSPs impact adherence?

  • The impact of Patient Support Programs varies significantly: some are able to considerably increase adherence rates while others only have limited impact.
  • Behavioral science theories and advances in digital technologies could be used to enhance PSP programs and improve their efficacy.

Non-adherence is a major issue that can be addressed by PSPs

Non-adherence impedes patients from benefiting from the clinical promise of their treatment. Medications that have proven their potential in clinical trials may not deliver on that potential in the real world, potentially because of non-adherence.1 The numbers speak for themselves: average adherence rates for chronic treatments range from 20% to 80% depending on the disease and the country, with an average of roughly 50%.2 As shown in previous articles in this series, low adherence rates have a significant impact on the quality of life for patients with neurological disorders, but also on society as a whole.3–5 The impact of this poor adherence includes premature deaths, increase in the number of otherwise avoidable hospitalizations, complications, etc.6–8 The cost for society is similarly high: medication non-adherence places a significant cost burden on healthcare systems.9 In the United States alone, the total healthcare cost impact is as high as $290 billion… simply because patients do not follow their treatment as prescribed.9 Non-adherence and its negative impact is observed all around the world.10

Why does it happen? People may not adhere to treatment for a variety of reasons: they might not understand the condition, prefer to ignore it, forget to take their medication, or in most cases, make a conscious decision not to follow their treatment for reasons that are their own and that they often rationalize.11,12 Whatever the reason, they adopt the “irrational” behavior of not obtaining the best health outcomes. To help patients change their behavior, authorities, the pharmaceutical industry, scientific medical societies and others have developed numerous strategies, ranging from simple approaches such as providing patient information services or developing apps to more sophisticated approaches such as connected devices and electronic pill boxes.13,14 While these connected tools typically allow the tracking of adherence, often, on their own, they do not significantly impact non- adherence, particularly in those patients who were not already motivated to be adherent.15 It goes without saying that the price of many such devices makes them impractical on a large scale.

With or without such devices, efforts to help patients follow their treatment and deal with their conditions are known as Patient Support Programs (PSPs).16 These programs employ a variety of strategies. Some are purely informative and provide practical information to patients on topics such as disease management, drug management (especially for complex drugs), etc. The channels through which patients receive this information might be limited to printed or electronic materials, but in many cases include web-based interventions.16 Such programs are designed to provide information to patients. While it is crucial to provide important information to patients, the impact on non- adherence of PSPs that limit themselves to information and patient education is often low. Note that this is not because the information is not important, but rather that it is typically not sufficient.17

Other PSPs provide purely behavioral tools, designed to influence patients’ attitude towards their disease and treatment management. These include treatment reminders, symptom trackers, etc.16 However, if we wish to affect the overall adherence rate of the patient population as a whole, we must also find ways to engage patients who would not otherwise be adherent. This can be best achieved by PSPs that are based on a mix of both informative and behavioral strategies and that fully incorporate the behavioral science principles that we have addressed throughout this series of articles.16

The impact of the PSP on adherence: A debatable topic

A 2017 meta-study on more than 700 PSP interventions implemented between the 1970s and 2010s showed a limited increase in adherence, if any, for patients.18 It determined that there were often biases in the reporting of such studies and that the most typical, or instinctual interventions, such as those designed to be delivered through the treating physicians, have particularly poor impact (although programs delivered through pharmacists seem to fare better).18 This is troubling to say the least for those who would like to affect adherence rates and it demonstrates that simply providing “more of the same” may be less than optimal.18

A 2015 meta-study of 17 selected PSPs covering more than 10,000 patients offers a more optimistic view. This study considered the influence on adherence of these PSPs for patients suffering from inflammatory and immunologic diseases such as rheumatoid arthritis, osteoporosis, ulcerative colitis, or multiple sclerosis.16 The 17 PSPs employed information strategies, behavioral strategies, or mixed strategies (behavioral and informative strategies).16

While this study was restricted to certain pathologies, the results lead to the conclusion that a careful mix of informational and behavioral strategies may be a means by which to generate significant impact and avoid the mixed results of less structured interventions.16

PSPs have also been utilized in neurological disorders; services such as phone and online resources, intensive reminders and active counselling have demonstrated a positive impact on adherence in conditions such as multiple sclerosis,19 Parkinson’s disease20 and epilepsy.21

The future of PSPs: Will personalization through behavioral sciences and digitalization increase adherence?

In 2003 the WHO cited RB Haynes in saying that “increasing the effectiveness of adherence interventions may have a far greater impact on the health of the population than any improvement in specific medical treatments”.22 Yet we have seen that these interventions, in the form of PSPs, are of debatable value if not carefully constructed. As such, the need for PSPs seems evident but they also need to be designed appropriately. One promising design approach is PSP personalization, discussed previously in this series. Through personalization, the mix between communication of information and more coaching-oriented approaches can be determined on an individual basis, while the specific information and behavioral content, as well as the tone of these interventions can be tailored to each individual.14

These types of highly personalized PSPs were rare in the past, due to the costs involved. However, the personalized approach can now be applied on a larger scale thanks to advances in digital and mobile technology.13 Furthermore, recent developments in the behavioral sciences, discussed in previous articles, allow greater understanding of the decision-making process of each patient and therefore can be far more effective in determining individual “nudges” for patients on the basis of their attitudinal profiles.14 Digital technologies that leverage behavioral science techniques effectively can produce significant improvements in personalization and patient experience.14,23

The effectiveness of personalized approaches using digital technologies has already been demonstrated in consumer-based industries such as e-commerce, banking, etc. Companies like Amazon and Netflix thrive thanks to the personalization approaches they use.24 The same principles can be applied to patients: consider a chat-bot with a virtual “patient-coach” that helps the patient manage his disease and treatment and is always available via his smartphone, tablet or computer.25 The patient responds to a questionnaire designed according to behavioral science theories and receives adherence “nudges” in the form of messages specifically adapted to his needs as well as his behavioral drivers.23

Such “chat-bots” have only recently been launched in the healthcare sphere, and their impact is yet to be measured. 23 However they open a whole new perspective for PSPs and therefore for addressing the non-adherence issue, allowing the positive influence of healthcare providers to be extended into the patient’s daily life.23

References

  1. Blonde L, et al. Interpretation and Impact of Real-World Clinical Data for the Practicing Clinician. Adv Ther 2018;35:1763–74.
  2. Capgemini Consulting. Estimated Annual Pharmaceutical Revenue Loss Due to Medication Non-Adherence. 2012.
  3. Parkinson’s UK. The cost of Parkinson’s: the financial impact of living with the condition. 2017. Available at: https://www.parkinsons.org.uk/sites/default/files/2017-07/CS2547%20Cost%20of%20Parkinson%27s%20report%202017%20-%20UK_1.pdf [Accessed April 2021].
  4. Malek N and Grosset DG. Medication adherence in patients with Parkinson’s disease. CNS Drugs 2015;29(1):47–53.
  5. Bessonova L, et al. The Economic Burden of Bipolar Disorder in the United States: A Systematic Literature Review. ClinicoEconomics and Outcomes Research 2020;12:481–97.
  6. O’Rourke G and O’Brien JJ. Identifying the barriers to antiepileptic drug adherence among adults with epilepsy. Seizure 2017;45:160–8.
  7. Faught E, et al. Nonadherence to antiepileptic drugs and increased mortality: findings from the RANSOM Study. Neurology 2008;71(20):1572–8.
  8. Fleischer JE and Stern MB. Medication Non-adherence in Parkinson‘s Disease. Curr Neurol Neurosci Rep 2013;13(10):10.1007/s11910-013-0382-z.
  9. Cutler RL, et al. Economic impact of medication non-adherence by disease groups: a systematic review. BMJ Open 2018;8:e016982.
  10. World Health Organization. Neurological Disorders: Public Health Challenges. 2006. Available at challenges/en/ [Accessed April 2021].
  11. Van den Bemt BJF, et al. Medication adherence in patients with rheumatoid arthritis: a critical appraisal of the existing literature. Expert Review of Clinical Immunology 2012;8(4):337–51.
  12. Molloy GJ, et al. Intentional and unintentional non-adherence to medications following an acute coronary syndrome: A longitudinal study. J Psychosom Res 2014;76(5):430–2.
  13. Gandapur Y, et al. The role of mHealth for improving medication adherence in patients with cardiovascular disease: a systematic review. Eur Heart J Qual Care Clin Outcomes 2016;2(4):237–44.
  14. Dolgin K. The SPUR Model: A Framework for Considering Patient Behavior. Patient Prefer Adherence 2020;14:97–105.
  15. Choudry NK, et al. Effect of Reminder Devices on Medication Adherence: The REMIND Randomized Clinical Trial. JAMA Intern Med 2017;177(5):624–31.
  16. Burudpakdee C, et al. Impact of patient programs on adherence and persistence in inflammatory and immunologic diseases: a meta-analysis. Patient Preference and Adherence 2015;9:435–48.
  17. Jackson C, et al. Applying COM-B to medication adherence: a suggested framework for research and interventions. Eur Health Psychol 2014;16(1):7–17.
  18. Conn VS and Ruppar TM. Medication adherence outcomes of 771 intervention trials: systematic review and meta-analysis. Prev Med 2017;99:269–76.
  19. Lenz F and Harms L. The Impact of Patient Support Programs on Adherence to Disease- Modifying Therapies of Patients With Relapsing-Remitting Multiple Sclerosis in Germany: A Non-Interventional, Prospective Study. Adv Ther 2020;37:2999–3009.
  20. Grosset KA and Grosset DG. Effect of educational intervention on medication timing in Parkinson’s disease: a randomized controlled trial. BMS Neurology 2007;7:20.
  21. Al-aqeel S and Al-sabhan J. Strategies for improving adherence to antiepileptic drug treatment in patients with epilepsy (review). Cochrane Database of Systematic Reviews 2011;Issue 1:CD008312.
  22. World Health Organization. Adherence to Long-Term Therapies: Evidence for Action. 2003. Available at: https://www.who.int/chp/knowledge/publications/adherence_report/en/ [Accessed April 2021]
  23. Nadarzynski T, et al. Acceptability of artificial intelligence (AI)-led chatbot services in healthcare: A mixed-methods study. Digit Health 2019;5:2055207619871808.
  24. Arora S. Recommendation Engines: How Amazon and Netflix Are Winning the Personalization Battle. 2016. Available at: www.martechadvisor.com/articles/customer-experience-2/recommendation-engines-how-amazon-and-netflix-are-winning-the-personalization-battle [Accessed April 2021].
  25. Chaix B, et al. When Chatbots Meet Patients : One-Year Prospective Study of Conversations Between Patients With Breast Cancer and a Chatbot. JMIR Cancer 2019;5(1):e12856.