







































Pallav Dave                                                                                                                                  Asian Journal of Dental and Health Sciences. 2024; 4(2):38-43 

[38]                                                                                                                                                                                                                                              AJDHS.COM 

 

 

Available online at ajdhs.com 

Asian Journal of Dental and Health Sciences 
Open Access to Dental and Medical Research 

Copyright  © 2024 The  Author(s): This is an open-access article distributed under the terms of the CC BY-NC 4.0 
which permits unrestricted use, distribution, and reproduction in any medium for non-commercial use provided 

the original author and source are credited  

 

 

Using AI to increase medication adherence 

Pallav Dave * 

Regulatory Compliance Analyst, Louisville, KY,40223, USA 

Article Info: 
_____________________________________________ 
Article History: 

Received   09 April 2024     
Reviewed  12 May 2024 
Accepted   04 June 2024 
Published 15 June 2024 

_____________________________________________ 
Cite this article as:  

Dave P, Using AI to increase medication adherence, 
Asian Journal of Dental and Health Sciences. 2024; 
4(2):38-43 

DOI: http://dx.doi.org/10.22270/ajdhs.v4i2.80     
_____________________________________________ 

*Address for Correspondence:   

Pallav Dave, Regulatory Compliance Analyst, 
Louisville, KY,40223, USA 

Abstract 
_________________________________________________________________________________________________________________ 

Although different measures have been taken to increase medication adherence, it still remains a 
significant challenge with research indicating that the rates of non-adherence remain as high as 40 
to 50%. Increasing medication adherence because non-adherence has a direct impact on patient 
outcomes. non-adherence contributes significantly to treatment failure. It also increases the rates of 
hospitalizations, mortality, and morbidity. Non-adherence also adds to healthcare costs affecting the 
ability of healthcare systems to provide the needed quality of care. Despite the implementation of 
traditional measures to increase adherence, these measures have led to mixed results. Most of these 
measures are limited because they rely on patient self-reports to measure adherence. They also do 
not verify whether a patient takes medication or not. Without verifying or confirming a patient has 
taken medication, it becomes significantly challenging to measure the rate of adherence. This 
necessitates the need for additional technologies to increase medication adherence. Leveraging 
technologies such as AI can help to address the limitations of traditional approaches to ensuring 
medication adherence. AI can be used to both predict adherence and improve adherence. However, 
to gain the full benefits offered by AI, it is important to address the challenges these technologies 
present such as ethical issues with regard to patient privacy and confidentiality of their data. The 
use of AI to increase medication adherence is also limited by limited knowledge and skills on how 
to use these technologies effectively and the type of technologies available. Therefore, this review 
explores how AI-based technologies can be used to increase medication adherence.  

Keywords: Medication adherence, non-adherence, Artificial Intelligence, patient outcomes, 
machine learning 

 

Introduction 

Medication adherence remains a significant challenge 
despite numerous measures taken to improve adherence and 
years of advocacy on the importance of the same. Globally, non-
adherence remains as high as 40% to 50%, particularly for 
patients grappling with chronic diseases.1,2 Medication 
adherence is imperative because non-adherence has a direct 
impact on patient outcomes.3 In fact, medication adherence has 
a more direct impact on patient outcomes than the treatment 
itself. In the United States, non-adherence accounts for up to 
50% of treatment failure, contributes to about 25% of 
hospitalizations and leads to up to 125,000 deaths.4 Despite the 
measures that have been taken to increase medication 
adherence, it remains a significant challenge mainly because 
the responsibility of medication adherence largely falls on 
patients. As a result, it because extremely challenging to attain 
significant success. Different factors contribute to the high rates 
of medication non-adherence. Patient-related barriers account 
for the highest cases of non-adherence. They include forgetting 
to take medications when expected, forgetting to go for refills, 
poor understanding of dosage and schedules, patient attitudes 
and beliefs about medication, side effects, impaired cognition, 
and lack of patient engagement in the treatment decisions 
among others.5  

Although the factors that contribute to non-adherence 
are known, addressing them is still a significant challenge. 
Different traditional measures of ensuring adherence such as 
pill counts, examining the rates of prescription refills, and 
electronic medication monitors have been taken over the years 

but despite this effort, the problem persists.6 This necessitates 
new measures to be adopted to increase medication adherence. 
The increasing adoption of technology in healthcare shows a lot 
of promise in different areas including medication adherence. 
According to research by Treskes et al., smart technology has 
been shown to increase medication adherence in patients with 
cardiovascular disease.7 The research investigated the potential 
of different types of technologies including phone 
interventions, short message service (SMS), smart pill boxes, 
web-based interventions, and mobile apps and showed that 
they had a lot of potential to increase adherence.7 Technology-
based methods of monitoring medication adherence show a lot 
of promise as they have been shown to have accuracy when it 
comes to monitoring adherence rates. However, successful 
implementation of these methods is still a challenge with 
results indicating mixed results when it comes to their efficacy 
in monitoring adherence.8,9 The mixed results on the 
effectiveness of these technologies require further research in 
this area.  

AI-powered technologies are coming up as better 
alternatives to improve medication adherence. Research has 
shown that these technologies improve medication adherence 
for different conditions.10,11,12,13 To aid in medication adherence, 
AI-powered tools can perform a number of functions. These 
functions include identifying non-adherent individuals, sending 
reminders, educating patients on the importance of medication 
adherence, and monitoring patients’ adherence.11 Identifying 
non-adherent individuals is important because it will lead to 
dedicated and personalized efforts to increase adherence. It is 

                     Open Access                                                                                                                                                                                                          Review Article                                                                           

http://jddtonline.info/
http://dx.doi.org/10.22270/ajdhs.v4i2.80
https://crossmark.crossref.org/dialog/?doi=10.22270/ajdhs.v4i2.80&amp;domain=pdf


Pallav Dave                                                                                                                                  Asian Journal of Dental and Health Sciences. 2024; 4(2):38-43 

[39]                                                                                                                                                                                                                                              AJDHS.COM 

also important to educate patients on the importance of 
adherence and send reminders. All these measures can help to 
increase adherence.  

This review seeks to explore how AI can be used to 
increase medication adherence. It bases its findings on past 
review papers that have documented the use of AI to increase 
medication adherence. As such, it explores both the positives 
and limitations of the same.  

Importance of Medication Adherence  

The World Health Organization recognizes the 
importance of medication adherence on disease outcomes by 
noting that non-adherence leads to poor patient outcomes and 
compounds the challenge of improving health.14 Medication 
non-adherence also results in wastage of the already limited 
healthcare resources. The problem of non-adherence is a 
significant challenge with research showing that up to 50% of 
people living with chronic illness do not take their medications 
as prescribed or expected.2,15,16 

Poor adherence has a negative impact on health. In 
addition to increasing morbidity and mortality rates, non-
adherence leads to wastage, disease progression, increased 
hospital visits and admissions, and lower quality of life.6,17,18 In 
addition to the negative impact on health, medication non-
adherence has significant cost implications. According to Cutler 
et al., the disease-specific economic cost of medication non-
adherence averages $949 to $44 190 per person.19 When it 
comes to all causes, the cost rises significantly to an average of 
$5271 to $52 341.19 These cost implications pose a significant 
burden to healthcare systems which are already burdened by 
high costs of care which impacts people’s ability to get 
medications for prescribed conditions.20 

Addressing non-compliance remains a significant 
challenge because it is attributed to different causes. The causes 
can be patient-related, treatment-related or even provider-
related. Patient-related non-adherence results from patient 
views and beliefs, social contexts, access or service issues.14 

Patients' views and beliefs play a crucial when it comes to non-
adherence. Beliefs and views about the illness can inform their 
decision to adhere to medication or not.14 For instance, if 
patients believe they need medication for their disease, then 
they are likely to adhere to it. Patients who believe the illness 
can go away without the medication are likely to be non-
adherent. Non-adherence can also be informed by patients' 
cultural or religious beliefs. Non-adherence may also be 
informed by the patients’ motivation to manage illness. If they 
are motivated to manage the illness, then they are likely to 
adhere to medication. Other factors such as forgetfulness, 
psychological stress, inadequate knowledge and skills, anxiety 
about medication adverse events, and lack of perceived need for 
treatment also inform patients’ decision to not adhere to 
medication. Inadequate knowledge and skills can affect 
patients’ ability to read and understand medication 
instructions which can inform decisions not to adhere to 
medication.6 Research has documented that patients who have 
low health literacy are likely to have negative disease 
outcomes.21,22 This is informed by factors such as the inability 
to understand medication instructions hence making them not 
to take medications or take them in a way that is not prescribed. 
Such can lead to negative outcomes. Fear of side effects is 
another commonly reported factor that contributes to 
medication non-adherence.23 Fear of side effects may be 
informed by factors such as negative reactions towards similar 
medication in the past, unpleasant withdrawal effects, and 
patient perceptions of the drugs.24  

Other patient-related factors that are attributed to 
poor medication adherence are complex regimens that involve 

taking many medications with varying dosage schedules, long-
term drug regimes, inadequate access, cost, inconvenience of 
taking medications, unpleasant taste, and busy work 
schedule.6,23 

Provider-related barriers can also contribute to poor 
medication adherence. Poor provider-patient communication is 
one of the provider-related barriers that is attributed to 
medication non-adherence.25 Effective communication is 
important to improve adherence because it enables patients to 
understand their illness better, the benefits of adhering to the 
medication regimen, and the risks that may arise if medication 
is not adhered to.26 Patient-provider communication is 
identified as instrumental in the patient-provider relationship 
which has been identified as a factor that informs medication 
non-adherence.27 For example, a poor-patient provider 
relationship can make it difficult for patients to ask relevant 
questions regarding their medication. Failing to clarify issues of 
concern can lead to confusion, misunderstanding, and 
subsequent non-adherence. Poor-patient-provider 
relationships may also make patients feel that doctors are not 
telling their patients the whole truth. Such feelings may inform 
non-adherence to the prescribed medications. Good 
communication makes patients feel more involved and are 
likely to take more control over their health including taking 
medications as prescribed. Provider-related barriers can also 
result from inadequate knowledge.28,29,30 Providers who lack 
adequate knowledge about a disease or the assigned treatment 
are less likely to provide patients with the needed information. 
This results in non-adherence.  

Patient-physician discordance has also been 
attributed to non-adherence. Patient-physician discordance is 
defined as the difference between patient and physician 
evaluations of health-related information.6 Such discordance 
leads to poor health outcomes. Research suggests patient-
physician discordance as one of the factors that lead to non-
adherence.6 If patients and physicians have different views of 
health-related information, conflict is likely to emerge affecting 
medication adherence.  

Limitations of Traditional Methods of Ensuring 
Medication Adherence 

Traditional measures of ensuring medication 
adherence have shown mixed results with some showing high 
success rates in increasing adherence while others showing 
very little success when it comes to increasing adherence. Some 
of the most commonly used measures to measure adherence 
are pill counts, self-reports, measures involving clinician 
assessment, patient diaries, and patient interviews.2,31 Some of 
these methods have had more success than others. For example, 
although pill count is a simple and low-cost method of assessing 
medication adherence, it has several limitations. Pill count is 
not feasible when it comes to assessing non-discrete dosages. 
Underestimation of adherence also occurs regularly because 
pill count does not consider the likelihood of having surplus 
medication. Other limitations that are associated with pill count 
are the inability to determine whether the dosage units 
removed are really taken and the inability to determine 
whether the patient is following the regimen even after the 
removal of the right number of units.32  

Clinician assessment and patient self-report have low 
success rates when it comes to measuring adherence rates. It is 
difficult to determine whether the patient is correctly reporting 
adherence by simply relying on an assessment of the same or a 
self-report.2 Patients may also have difficulty communicating or 
answering the questions as expected. However, despite being 
highly unreliable, they are still preferred because of low cost, 
simplicity, and real-time feedback. Patient diaries are also a 



Pallav Dave                                                                                                                                  Asian Journal of Dental and Health Sciences. 2024; 4(2):38-43 

[40]                                                                                                                                                                                                                                              AJDHS.COM 

simple method to measure adherence but they are unreliable, 
limited by overestimation, and false reporting.33  

Medication events monitoring systems (MEMS) have 
shown more accuracy when it comes to medication adherence 
compared to methods such as pill counts, patient diaries, and 
self-reports. Research has shown that MEMS is able to 
determine non-adherence because it records both the time and 
date when medication is taken from a container.2 As such, it is 
able to establish whether there is an abnormal medication-
taking pattern or the number of doses missed. Research has 
shown that the ability to note the exact time and date the 
medication is taken from the bottle makes it more reliable that 
patient diaries or self-report. Besides, research has shown that 
MEMS helps to increase adherence.34,35  

The limitations of traditional measures of medication 
adherence are what inform the need to explore other measures 
to increase adherence. AI-powered measures are being 
considered valuable tools to increase adherence by both 
predicting adherence and providing a comprehensive 
assessment of patients’ adherence behavior.36 AI has been 
explored for different conditions including chronic diseases. In 
some instances, it has been shown to increase adherence by up 
to 70 to 80%.37 With such high adherence rates, AI tools are 
likely to be a good alternative to traditional methods of ensuring 
medication adherence.  

The Role of AI in Increasing Medication 
Adherence  

Capitalizing on AI and machine learning-based 
technologies to increase medication adherence can have a 
positive impact on patient outcomes. AI-based tools and 
technologies together with traditional tools of ensuring 
medication adherence can make a significant impact in this area 
and lead to positive outcomes. There is research documenting 
the use of different AI-powered technologies in ensuring 
medication adherence.11 The technology has a lot of potential 
when it comes to increasing adherence because it leverages 
data to offer personalized and intelligent support to patients 
that can aid in behavioral change and lead to increased 
adherence. AI-powered technologies and tools have 
documented varied levels of success in different areas of 
medication adherence including prediction, reminders, and 
monitoring of patient adherence.  

One of the areas where AI-based tools and 
technologies are being used is to predict medication adherence 
and non-adherence. Predicting adherence or non-adherence is 
important in increasing medication adherence. It helps to 
determine the patient’s likelihood of adhering to the prescribed 
medication regimen. According to Koesmahargyo et al., the 
prediction of medication adherence traditionally relies on the 
assessment of factors such as tolerability, treatment length, and 
demographics.38 However, with the introduction of 
technologies with the ability to automatically measure 
medication dosing in real-time, it becomes easier to measure 
adherence. Such technologies rely on real-time data. As such, it 
becomes much easier to predict whether the patient will adhere 
to medication or not. AI-based technologies provide accuracy 
when it comes to making such predictions. Gu et al. suggest that 
predicting medication adherence can be done using ensemble 
learning and deep learning models.39These models provide an 
opportunity to identify patients who have a high risk of non-
adherence within a specific time frame. Wu et al. found that 
using machine learning algorithms to predict non-adherence in 
type 2 diabetes patients helped to identify risk factors that 
increased the likelihood of non-adherence.40 Identifying risk 
factors made it easier to provide individualized diabetes care 
and education hence increasing the likelihood of adherence. 
Similarly, Li et al. established that machine learning could be 

used to predict the risk of non-adherence in patients with type 
2 diabetes.41 Predicting medication adherence and non-
adherence is imperative because it helps to identify factors that 
make it difficult for patients to adhere to medications and 
design personalized solutions to the problem. For instance, it 
becomes easier to tailor patient education and care if factors 
that make it difficult to adhere to medication are known.  

In addition to predicting adherence levels, AI-
powered tools can be used to increase adherence. Research has 
documented the success of AI when it comes to improving 
medication adherence.11,12 Labovitz et al. showed that AI 
improved adherence in patients on anticoagulation therapy by 
up to 50%.12 The study showed that even patients who had 
limited experience with using smartphones were able to use the 
technology effectively to increase their adherence. The study 
attributed the improvement in adherence to a number of factors 
including real-time monitoring, ability to accurately monitor 
medication ingestion, and change in patient behavior. AI-based 
technologies can also be used to monitor patient compliance. A 
novel artificial intelligence platform on mobile devices 
increased dosing compliance by 17.9% in patients with 
schizophrenia.42 For patients who were monitored using the AI 
platform, the compliance was 89.7% which was higher than the 
71.9% that was recorded for patients who were not monitored 
using AI.42 The AI platform collected data on the date and time 
stamps the medication was taken for each pill. It also collected 
data on missed doses, skipped doses, and doses taken in the 
clinic. Other data that was collected was visual confirmation of 
the patient ingesting the drug which was done using the tool. 
Visual confirmation increased the accuracy of the platform 
when compared to traditional methods of monitoring 
medication adherence such as pill counts or patients’ self-
reports. 

AI-powered technologies can also be used to increase 
medication adherence in conditions that require long-term 
therapies such as tuberculosis (TB) and chronic diseases. Long-
term drug regimens are recognized as a barrier to medication 
adherence.6 Diseases such as TB are managed through long-
term therapies with some therapies lasting up to 6 months. 
These long-term therapies pose a significant challenge to 
patients which explains why non-adherence is commonly 
reported in TB patients.43 Considering the importance of 
adhering to medication when it comes to the management of 
TB, it is important to use technology to increase adherence. One 
study has documented the significance of using AI algorithms to 
increase medication adherence in patients living with TB.44 The 
study documented success with AI algorithms resulting in 
effective management of TB therapies. Besides, research has 
documented that digital adherence technologies have high 
success rates in increasing adherence to TB therapy particularly 
when they are integrated with clinical strategies.45,46 Some of 
these technologies are powered by AI. Other areas where AI can 
be used to increase medication adherence are to increase 
patients’ rates of refill, monitor patients’ adherence, and 
monitor adverse events. 

AI has a lot of potential when it comes to increasing 
medication adherence. Leveraging this potential can lead to 
positive outcomes for both patients and healthcare systems. AI-
powered technologies can be used to address patient-related 
barriers that affect medication adherence such as forgetfulness 
and poor reporting. Leveraging AI can also help to reduce costs 
associated with medication non-adherence. Some studies have 
documented that AI-powered tools significantly increase 
adherence which means that they can help to fill the gaps left by 
traditional methods of measuring adherence such as pill counts, 
patient self-reports, and clinical assessment. However, to gain 
the full benefits of AI, there is a need to address the challenges 
posed by AI use in monitoring medication adherence.  



Pallav Dave                                                                                                                                  Asian Journal of Dental and Health Sciences. 2024; 4(2):38-43 

[41]                                                                                                                                                                                                                                              AJDHS.COM 

Challenges of Using AI to Increase Medication 
Adherence 

Although AI shows a lot of potential in addressing 
medication adherence challenges, it presents unique barriers 
that need to be addressed to optimize its potential. One of the 
unique barriers presented by AI use in medication adherence is 
ethical issues.11 The use of AI in healthcare has always 
presented a challenge when it comes to patient data, privacy, 
and confidentiality.47 AI and machine learning deal with large 
datasets most of which contain patient-sensitive information. 
As a result, securing patient privacy and confidentiality when it 
comes to the use of this data becomes challenging. Securing 
patient privacy and confidentiality when it comes to the use of 
AI in ensuring medication adherence becomes particularly 
challenging considering such technologies require the use of 
patient data to be effective. For instance, to ensure patients 
adhere to medication, it is important to have personal data on 
these patients. Securing this data and ensuring patient 
anonymity can be challenging limiting the use of AI to increase 
medication adherence.  

An additional issue that emerges from the use of AI to 
increase medication adherence is the lack of knowledge and 
skills on how to effectively use these technologies. Research has 
documented limited skill sets as one of the challenges that limit 
the use of AI in healthcare. A lack of knowledge and skills on 
how to use these technologies to monitor adherence can limit 
their use and implementation.36 Limitations of knowledge on 
the available AI-based technologies also limit their use. 
Although significant advances have been made in the 
development of AI-based solutions for different areas of 
healthcare, most of these technologies are not known to 
healthcare providers limiting their use. Addressing the 
limitations in knowledge and skillsets can increase AI use in 
monitoring medication adherence.  

Conclusion 

Increasing medication adherence is vital to improve 
patient outcomes. However, despite the implementation of 
different measures to improve adherence, it still remains a 
significant challenge with these measures producing mixed 
results. Most of the traditional measures of ensuring 
medication adherence are limited by the fact that they do not 
verify medication administration. Without verifying medication 
administration, it becomes really challenging to measure 
adherence. Increasing medication adherence is recognized as 
instrumental in improving patient outcomes. Non-adherence 
also compounds the challenge of improving health. Other 
problems attributed to non-adherence are increased morbidity, 
increased mortality, increased hospital visits and admissions, 
significant disease progression, and poor quality of life. Non-
adherence also costs healthcare systems billions of dollars each 
year. Leveraging technologies such as AI can help to address the 
limitations of traditional approaches to ensuring medication 
adherence. AI-powered technologies are being used in different 
areas of healthcare and medication adherence is not an 
exemption. AI can be used to both predict adherence and 
improve adherence. However, to gain the full benefits offered by 
AI, it is important to address the challenges these technologies 
present. For instance, ethical issues remain a prevalent problem 
when it comes to AI use in healthcare. Maintaining patient 
privacy and confidentiality on sensitive issues is a significant 
problem. The use of AI to increase medication adherence is also 
limited by limited knowledge and skills on how to use this 
technology effectively.  

References 

1. Lee EK, Poon P, Yip BH, et al. Global burden, regional differences, 
trends, and health consequences of medication nonadherence for 

hypertension during 2010 to 2020: a meta-analysis involving 27 
million patients. Journal of the American Heart Association. 
2022;11(17):e026582.  
https://doi.org/10.1161/JAHA.122.026582 PMid:36056737 
PMCid:PMC9496433 

2. Lam WY, Fresco P. Medication adherence measures: an overview. 
Biomed Research International. 2015;2015(1):217047. 
https://doi.org/10.1155/2015/217047 PMid:26539470 
PMCid:PMC4619779 

3. Brown MT, Bussell JK. Medication adherence: WHO cares?. In Mayo 
Clinic Proceedings 2011 Apr 1 (Vol. 86, No. 4, pp. 304-314). 
Elsevier. https://doi.org/10.4065/mcp.2010.0575 
PMid:21389250 PMCid:PMC3068890 

4. Kim J, Combs K, Downs J, Tillman F. Medication adherence: The 
elephant in the room. Us Pharm. 2018;43(1):30-4. 

5. Neiman AB. CDC grand rounds: improving medication adherence for 
chronic disease management-innovations and opportunities. 
MMWR. Morbidity and Mortality Weekly Report. 2017;66. 
https://doi.org/10.15585/mmwr.mm6645a2 PMid:29145353 
PMCid:PMC5726246 

6. Jimmy B, Jose J. Patient medication adherence: measures in daily 
practice. Oman Medical Journal. 2011;26(3):155. 
https://doi.org/10.5001/omj.2011.38 PMid:22043406 
PMCid:PMC3191684 

7. Treskes RW, Van der Velde ET, Schoones JW, Schalij MJ. 
Implementation of smart technology to improve medication 
adherence in patients with cardiovascular disease: is it effective?. 
Expert Review of Medical Devices. 2018;15(2):119-26. 
https://doi.org/10.1080/17434440.2018.1421456 
PMid:29271661 

8. Dave P, How Digital Health is Revolutionizing Healthcare and 
Contributing to Positive Health Outcomes, Journal of Drug 
Delivery and Therapeutics. 2024; 14(6):287-293 
https://doi.org/10.22270/jddt.v14i6.6640 

9. Mason M, Cho Y, Rayo J, Gong Y, Harris M, Jiang Y. Technologies for 
medication adherence monitoring and technology assessment 
criteria: narrative review. JMIR Mhealth and Uhealth. 
2022;10(3):e35157. https://doi.org/10.2196/35157 
PMid:35266873 PMCid:PMC8949687 

10. Bohlmann A, Mostafa J, Kumar M. Machine learning and 
medication adherence: scoping review. JMIRx Med. 
2021;2(4):e26993. https://doi.org/10.2196/26993 
PMid:37725549 PMCid:PMC10414315 

11. Babel A, Taneja R, Mondello Malvestiti F, Monaco A, Donde S. 
Artificial intelligence solutions to increase medication adherence 
in patients with non-communicable diseases. Frontiers in Digital 
Health. 2021;3:669869. 
https://doi.org/10.3389/fdgth.2021.669869 PMid:34713142 
PMCid:PMC8521858 

12. Labovitz DL, Shafner L, Reyes Gil M, Virmani D, Hanina A. Using 
artificial intelligence to reduce the risk of nonadherence in 
patients on anticoagulation therapy. Stroke. 2017;48(5):1416-9. 
https://doi.org/10.1161/STROKEAHA.116.016281 
PMid:28386037 PMCid:PMC5432369 

13. Pinho S, Cruz M, Ferreira F, Ramalho A, Sampaio R. Improving 
medication adherence in hypertensive patients: A scoping review. 
Preventive Medicine. 2021;146:106467. 
https://doi.org/10.1016/j.ypmed.2021.106467 PMid:33636195 

14. World Health Organization. Adherence to long-term therapies: 
evidence for action. World Health Organization. 2003. Available 
from: https://iris.who.int/handle/10665/42682  

15. Glombiewski JA, Nestoriuc Y, Rief W, Glaesmer H, Braehler E. 
Medication adherence in the general population. PLoS One. 
2012;7(12):e50537. 
https://doi.org/10.1371/journal.pone.0050537 PMid:23272064 
PMCid:PMC3525591 

16. Yeaw J, Benner JS, Walt JG, Sian S, Smith DB. Comparing adherence 
and persistence across 6 chronic medication classes. Journal of 
Managed Care Pharmacy. 2009;15(9):728-40. 

https://doi.org/10.1161/JAHA.122.026582
https://doi.org/10.1155/2015/217047
https://doi.org/10.4065/mcp.2010.0575
https://doi.org/10.15585/mmwr.mm6645a2
https://doi.org/10.5001/omj.2011.38
https://doi.org/10.1080/17434440.2018.1421456
https://doi.org/10.22270/jddt.v14i6.6640
https://doi.org/10.2196/35157
https://doi.org/10.2196/26993
https://doi.org/10.3389/fdgth.2021.669869
https://doi.org/10.1161/STROKEAHA.116.016281
https://doi.org/10.1016/j.ypmed.2021.106467
https://iris.who.int/handle/10665/42682
https://doi.org/10.1371/journal.pone.0050537


Pallav Dave                                                                                                                                  Asian Journal of Dental and Health Sciences. 2024; 4(2):38-43 

[42]                                                                                                                                                                                                                                              AJDHS.COM 

https://doi.org/10.18553/jmcp.2009.15.9.728 PMid:19954264 
PMCid:PMC10441195 

17. Mongkhon P, Ashcroft DM, Scholfield CN, Kongkaew C. Hospital 
admissions associated with medication non-adherence: a 
systematic review of prospective observational studies. BMJ 
Quality & Safety. 2018;27(11):902-14. 
https://doi.org/10.1136/bmjqs-2017-007453 PMid:29666309 

18. Dave P, How AI Can Revolutionize the Pharmaceutical Industry. 
Journal of Drug Delivery and Therapeutics. 2024; 14(6):179-183. 
https://doi.org/10.22270/jddt.v14i6.6657 

19. Cutler RL, Fernandez-Llimos F, Frommer M, Benrimoj C, Garcia-
Cardenas V. Economic impact of medication non-adherence by 
disease groups: a systematic review. BMJ Open. 
2018;8(1):e016982. https://doi.org/10.1136/bmjopen-2017-
016982 PMid:29358417 PMCid:PMC5780689 

20. Van Alsten SC. Cost-related nonadherence and mortality in 
patients with chronic disease: a multiyear investigation, National 
Health Interview Survey, 2000-2014. Preventing Chronic Disease. 
2020;17. https://doi.org/10.5888/pcd17.200244 PMid:33274701 
PMCid:PMC7735485 

21. Berkman ND, Sheridan SL, Donahue KE, Halpern DJ, Crotty K. Low 
health literacy and health outcomes: an updated systematic 
review. Annals of Internal Medicine. 2011;155(2):97-107. 
https://doi.org/10.7326/0003-4819-155-2-201107190-00005 
PMid:21768583 

22. Shahid R, Shoker M, Chu LM, Frehlick R, Ward H, Pahwa P. Impact 
of low health literacy on patients' health outcomes: a multicenter 
cohort study. BMC Health Services Research. 2022;22(1):1148. 
https://doi.org/10.1186/s12913-022-08527-9 PMid:36096793 
PMCid:PMC9465902 

23. Khan MU, Shah S, Hameed T. Barriers to and determinants of 
medication adherence among hypertensive patients attended 
National Health Service Hospital, Sunderland. Journal of Pharmacy 
and Bioallied Sciences. 2014;6(2):104-8. 
https://doi.org/10.4103/0975-7406.129175 PMid:24741278 
PMCid:PMC3983739 

24. Smith LE, Webster RK, Rubin GJ. A systematic review of factors 
associated with side-effect expectations from medical 
interventions. Health Expectations. 2020;23(4):731-58. 
https://doi.org/10.1111/hex.13059 PMid:32282119 
PMCid:PMC7495066 

25. Ghosh P, Balasundaram S, Sankaran A, Chandrasekaran V, Sarkar S, 
Choudhury S. Factors associated with medication non-adherence 
among patients with severe mental disorder-a cross sectional 
study in a tertiary care centre. Exploratory Research in Clinical 
and Social Pharmacy. 2022;7:100178. 
https://doi.org/10.1016/j.rcsop.2022.100178 PMid:36161207 
PMCid:PMC9493377 

26. Świątoniowska-Lonc N, Polański J, Tański W, Jankowska-Polańska 
B. Impact of satisfaction with physician-patient communication on 
self-care and adherence in patients with hypertension: cross-
sectional study. BMC Health Services Research. 2020;20:1-9. 
https://doi.org/10.1186/s12913-020-05912-0 PMid:33198739 
PMCid:PMC7670590 

27. Stavropoulou C. Non-adherence to medication and doctor-patient 
relationship: Evidence from a European survey. Patient Education 
and Counseling. 2011;83(1):7-13. 
https://doi.org/10.1016/j.pec.2010.04.039 PMid:20541884 

28. Dave P, The Vital Role of Pharmacists in Diabetes Self-care, Journal 
of Drug Delivery and Therapeutics. 2024; 14(5):229-233 
https://doi.org/10.22270/jddt.v14i5.6582 

29. Krishnamoorthy Y, Rajaa S, Rehman T, Thulasingam M. Patient and 
provider's perspective on barriers and facilitators for medication 
adherence among adult patients with cardiovascular diseases and 
diabetes mellitus in India: a qualitative evidence synthesis. BMJ 
Open. 2022;12(3):e055226. https://doi.org/10.1136/bmjopen-
2021-055226 PMid:35332041 PMCid:PMC8948385 

30. Brown MT, Bussell J, Dutta S, Davis K, Strong S, Mathew S. 
Medication adherence: truth and consequences. The American 

Journal of the Medical Sciences. 2016;351(4):387-99. 
https://doi.org/10.1016/j.amjms.2016.01.010 PMid:27079345 

31. Ho PM, Bryson CL, Rumsfeld JS. Medication adherence: its 
importance in cardiovascular outcomes. Circulation. 
2009;119(23):3028-35. 
https://doi.org/10.1161/CIRCULATIONAHA.108.768986 
PMid:19528344 

32. Van Onzenoort HA, Verberk WJ, Kessels AG, et al. Assessing 
medication adherence simultaneously by electronic monitoring 
and pill count in patients with mild-to-moderate hypertension. 
American Journal of Hypertension. 2010;23(2):149-54. 
https://doi.org/10.1038/ajh.2009.207 PMid:19927136 

33. Nunes V, Neilson J, O'flynn N, et al. Medicines adherence: involving 
patients in decisions about prescribed medicines and supporting 
adherence. Other. NICE: National Institute for Health and Clinical 
Excellence, London, UK. Available from: 
https://www.ncbi.nlm.nih.gov/books/NBK55447/  

34. van Heuckelum M, van den Ende CH, Houterman AE, Heemskerk 
CP, van Dulmen S, van den Bemt BJ. The effect of electronic 
monitoring feedback on medication adherence and clinical 
outcomes: A systematic review. Plos One. 2017;12(10):e0185453. 
https://doi.org/10.1371/journal.pone.0185453 PMid:28991903 
PMCid:PMC5633170 

35. Demonceau J, Ruppar T, Kristanto P, et al. Identification and 
assessment of adherence-enhancing interventions in studies 
assessing medication adherence through electronically compiled 
drug dosing histories: a systematic literature review and meta-
analysis. Drugs. 2013;73:545-62. 
https://doi.org/10.1007/s40265-013-0041-3 PMid:23588595 
PMCid:PMC3647098 

36. Pulice E, Coustasse A. AI-Driven solutions promote medication 
adherence. Pharmacy Times. Available from: 
https://www.pharmacytimes.com/view/ai-driven-solutions-
promote-medication-adherence  

37. Warren D, Marashi A, Siddiqui A, et al. Using machine learning to 
study the effect of medication adherence in Opioid Use Disorder. 
Plos One. 2022;17(12):e0278988. 
https://doi.org/10.1371/journal.pone.0278988 PMid:36520864 
PMCid:PMC9754174 

38. Koesmahargyo V, Abbas A, Zhang L, et al. Accuracy of machine 
learning-based prediction of medication adherence in clinical 
research. Psychiatry Research. 2020;294:113558. 
https://doi.org/10.1016/j.psychres.2020.113558 PMid:33242836 

39. Gu Y, Zalkikar A, Liu M, et al. Predicting medication adherence 
using ensemble learning and deep learning models with large 
scale healthcare data. Scientific Reports. 2021;11(1):18961. 
https://doi.org/10.1038/s41598-021-98387-w PMid:34556746 
PMCid:PMC8460813 

40. Wu XW, Yang HB, Yuan R, Long EW, Tong RS. Predictive models of 
medication non-adherence risks of patients with T2D based on 
multiple machine learning algorithms. BMJ Open Diabetes 
Research and Care. 2020;8(1):e001055. 
https://doi.org/10.1136/bmjdrc-2019-001055 PMid:32156739 
PMCid:PMC7064141 

41. Li M, Lu X, Yang H, et al. Development and assessment of novel 
machine learning models to predict medication non-adherence 
risks in type 2 diabetics. Frontiers in Public Health. 
2022;10:1000622. https://doi.org/10.3389/fpubh.2022.1000622 
PMid:36466490 PMCid:PMC9714465 

42. Bain EE, Shafner L, Walling DP, Othman AA, Chuang-Stein C, Hinkle 
J, Hanina A. Use of a novel artificial intelligence platform on 
mobile devices to assess dosing compliance in a phase 2 clinical 
trial in subjects with schizophrenia. JMIR mHealth and uHealth. 
2017;5(2):e7030. https://doi.org/10.2196/mhealth.7030 
PMid:28223265 PMCid:PMC5340925 

43. Sazali MF, Rahim SS, Mohammad AH, et al. Improving tuberculosis 
medication adherence: the potential of integrating digital 
technology and health belief model. Tuberculosis and Respiratory 
Diseases. 2023;86(2):82. https://doi.org/10.4046/trd.2022.0148 
PMid:36597583 PMCid:PMC10073608 

https://doi.org/10.18553/jmcp.2009.15.9.728
https://doi.org/10.1136/bmjqs-2017-007453
https://doi.org/10.22270/jddt.v14i6.6657
https://doi.org/10.1136/bmjopen-2017-016982
https://doi.org/10.1136/bmjopen-2017-016982
https://doi.org/10.5888/pcd17.200244
https://doi.org/10.7326/0003-4819-155-2-201107190-00005
https://doi.org/10.1186/s12913-022-08527-9
https://doi.org/10.4103/0975-7406.129175
https://doi.org/10.1111/hex.13059
https://doi.org/10.1016/j.rcsop.2022.100178
https://doi.org/10.1186/s12913-020-05912-0
https://doi.org/10.1016/j.pec.2010.04.039
https://doi.org/10.22270/jddt.v14i5.6582
https://doi.org/10.1136/bmjopen-2021-055226
https://doi.org/10.1136/bmjopen-2021-055226
https://doi.org/10.1016/j.amjms.2016.01.010
https://doi.org/10.1161/CIRCULATIONAHA.108.768986
https://doi.org/10.1038/ajh.2009.207
https://www.ncbi.nlm.nih.gov/books/NBK55447/
https://doi.org/10.1371/journal.pone.0185453
https://doi.org/10.1007/s40265-013-0041-3
https://www.pharmacytimes.com/view/ai-driven-solutions-promote-medication-adherence
https://www.pharmacytimes.com/view/ai-driven-solutions-promote-medication-adherence
https://doi.org/10.1371/journal.pone.0278988
https://doi.org/10.1016/j.psychres.2020.113558
https://doi.org/10.1038/s41598-021-98387-w
https://doi.org/10.1136/bmjdrc-2019-001055
https://doi.org/10.3389/fpubh.2022.1000622
https://doi.org/10.2196/mhealth.7030
https://doi.org/10.4046/trd.2022.0148


Pallav Dave                                                                                                                                  Asian Journal of Dental and Health Sciences. 2024; 4(2):38-43 

[43]                                                                                                                                                                                                                                              AJDHS.COM 

44. Kim K, Kim B, Chung AJ, Kwon K, Choi E, Nah JW. Algorithm and 
System for improving the medication adherence of tuberculosis 
patients. In2018 International Conference on Information and 
Communication Technology Convergence (ICTC). 2018 (pp. 914-
916). IEEE. https://doi.org/10.1109/ICTC.2018.8539402 

45. Liu X, Thompson J, Dong H, Sweeney S, Li X, Yuan Y, Wang X, He W, 
Thomas B, Xu C, Hu D. Digital adherence technologies to improve 
tuberculosis treatment outcomes in China: a cluster-randomised 
superiority trial. The Lancet Global Health. 2023;11(5):e693-703. 
https://doi.org/10.1016/S2214-109X(23)00068-2 
PMid:37061308 

46. Subbaraman R, de Mondesert L, Musiimenta A, Pai M, Mayer KH, 
Thomas BE, Haberer J. Digital adherence technologies for the 
management of tuberculosis therapy: mapping the landscape and 
research priorities. BMJ Global Health. 2018;3(5):e001018. 
https://doi.org/10.1136/bmjgh-2018-001018 PMid:30364330 
PMCid:PMC6195152 

47. Lee D, Yoon SN. Application of artificial intelligence-based 
technologies in the healthcare industry: Opportunities and 
challenges. International Journal of Environmental Research and 
Public Health. 2021;18(1):271. 
https://doi.org/10.3390/ijerph18010271 PMid:33401373 
PMCid:PMC7795119 

 

https://doi.org/10.1109/ICTC.2018.8539402
https://doi.org/10.1016/S2214-109X(23)00068-2
https://doi.org/10.1136/bmjgh-2018-001018
https://doi.org/10.3390/ijerph18010271

