This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIRx Med, is properly cited. The complete bibliographic information, a link to the original publication on https://med.jmirx.org/, as well as this copyright and license information must be included.
This is the first scoping review to focus broadly on the topics of machine learning and medication adherence.
This review aims to categorize, summarize, and analyze literature focused on using machine learning for actions related to medication adherence.
PubMed, Scopus, ACM Digital Library, IEEE, and Web of Science were searched to find works that meet the inclusion criteria. After full-text review, 43 works were included in the final analysis. Information of interest was systematically charted before inclusion in the final draft. Studies were placed into natural categories for additional analysis dependent upon the combination of actions related to medication adherence. The protocol for this scoping review was created using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines.
Publications focused on predicting medication adherence have uncovered 20 strong predictors that were significant in two or more studies. A total of 13 studies that predicted medication adherence used either self-reported questionnaires or pharmacy claims data to determine medication adherence status. In addition, 13 studies that predicted medication adherence did so using either logistic regression, artificial neural networks, random forest, or support vector machines. Of the 15 studies that predicted medication adherence, 6 reported predictor accuracy, the lowest of which was 77.6%. Of 13 monitoring systems, 12 determined medication administration using medication container sensors or sensors in consumer electronics, like smartwatches or smartphones. A total of 11 monitoring systems used logistic regression, artificial neural networks, support vector machines, or random forest algorithms to determine medication administration. The 4 systems that monitored inhaler administration reported a classification accuracy of 93.75% or higher. The 2 systems that monitored medication status in patients with Parkinson disease reported a classification accuracy of 78% or higher. A total of 3 studies monitored medication administration using only smartwatch sensors and reported a classification accuracy of 78.6% or higher. Two systems that provided context-aware medication reminders helped patients to achieve an adherence level of 92% or higher. Two conversational artificial intelligence reminder systems significantly improved adherence rates when compared against traditional reminder systems.
Creation of systems that accurately predict medication adherence across multiple data sets may be possible due to predictors remaining strong across multiple studies. Higher quality measures of adherence should be adopted when possible so that prediction algorithms are based on accurate information. Currently, medication adherence can be predicted with a good level of accuracy, potentially allowing for the development of interventions aimed at preventing nonadherence. Monitoring systems that track inhaler use currently classify inhaler-related actions with an excellent level of accuracy, allowing for tracking of adherence and potentially proper inhaler technique. Systems that monitor medication states in patients with Parkinson disease can currently achieve a good level of classification accuracy and have the potential to inform medication therapy changes in the future. Medication administration monitoring systems that only use motion sensors in smartwatches can currently achieve a good level of classification accuracy but only when differentiating between a small number of possible activities. Context-aware reminder systems can help patients achieve high levels of medication adherence but are also intrusive, which may not be acceptable to users. Conversational artificial intelligence reminder systems can significantly improve adherence.
Health care costs will continue to rise into the foreseeable future unless technology is implemented that substantially increases the efficiency of care delivery. Machine learning is a technology with the potential to automate many health care processes, including actions that impact medication adherence. Medication adherence is an important issue because approximately 50% of patients with chronic disease are not adherent to their medications, thus increasing medical costs and avoidable human suffering [
Recently, reviews were published that discuss the effectiveness of using machine learning to improve medication adherence. Awan et al [
In contrast to previous works, this review is focused more generally on the use of machine learning within the confines of medication adherence. By using a broader perspective, this paper provides high-level insight into this area of study, which is not possible with more narrowly focused examinations. Medication adherence is a complex problem that can be engaged from multiple angles using machine learning. This paper serves as a way to quickly learn about different approaches, their current level of development, and obstacles that need to be overcome to use machine learning more effectively toward improving medication adherence.
This review also categorizes and summarizes how machine learning has been used to execute actions related to medication adherence in academic literature. Within each category, common themes will be explored to find gaps and opportunities for future work.
The eligibility criteria were developed through collaboration between authors AB and JM. Papers included in this review carried out at least one action related to machine learning and medication adherence. Studies also had to test out their application of machine learning to medication adherence using either real patients, research participants, or simulations. The ABC taxonomy was used to define medication adherence for this study. This taxonomy defines medication adherence as “the process by which patients take their medications as prescribed, composed of initiation, implementation and discontinuation” [
The following databases were searched to identify relevant papers: PubMed, Scopus, IEEE, ACM Digital Library, and Web of Science. These databases were selected by the Laboratory of Applied Informatics Research (LAIR) team as representing the largest and most extensive coverage of studies that investigate applying machine learning to medication adherence.
Search queries for this paper were created with the help of Rebecca Carlson and Fei Yu, both of whom are experienced research librarians. Search terms were selected by reviewing exhaustive term lists provided by the research librarians on the topics of machine learning and medication adherence. The same keywords were selected for all five databases by combining the lists of machine learning and medication adherence terms using an AND operator. The final search was conducted on April 30, 2020. When searching PubMed, Medical Subject Headings (MeSH) terms were incorporated to provide broad coverage of related vocabulary. Following PubMed, this same query strategy was used to search Scopus but with Emtree Terms in place of the MeSH terms. For IEEE, ACM Digital Library, and Web of Science, the keywords were used exclusively to discover relevant papers. Examples of the search queries are provided in section A of
The search queries found 504 studies that contain both machine learning and medication adherence terms. After removing duplicate studies, 417 papers were identified for review. The title and abstract review were conducted using a two-person team consisting of authors AB and MK. The title and abstract evaluation reduced the total number of relevant papers to 54. Next, a sample of 20 articles was screened. This initial screening was done to ensure that information was collected in a consistent manner using a data charting template. This template was developed through collaboration with the LAIR team and the primary author of this work. After finalization of the data charting template, all 43 papers were reviewed in full by AB and MK. Studies that passed the full-text review focused on both medication adherence and machine learning. A focus on medication adherence and machine learning was essential to reduce the number of studies to a manageable list of highly relevant works. Included works also evaluated the use of machine learning for medication adherence actions using either patients, research participants, or simulations. This testing requirement was used to exclude studies that are still in the early development stages and to make sure that included works contained all of the relevant data charting elements. Next, the results were grouped into natural categories and analyzed according to the combination of medication adherence actions in each study. This grouping of related studies was carried out by AB, JM, and MK.
Article review process.
A data charting form was created and updated throughout the review process as needed. This form was used to guide the selection of relevant information throughout the review process and was tested to ensure consistency across different articles during the sample screening. The items included in the data charting template are listed in the next section.
Data charted for this review includes the article title, publication date, study number, study goals, main study results, disease states, predictors of medication adherence, types of machine learning used, number of participants, data collection methods, actions related to medication adherence, adherence measurements, limitations, and inclusion or exclusion status.
Following data charting, actions related to medication adherence were determined for each study. Next, articles were grouped for further analysis according to the combinations of medication adherence actions included in each work. AB, JM, and MK conducted the categorization process, and disagreements were discussed until a consensus was reached. Following categorization, the data charting documents for these works were reviewed to find relevant themes. A general analysis was also conducted before categorization in terms of the number of publications per year, articles per disease state, and publications per database type.
This paper includes the analysis of 43 studies selected for inclusion. The main results of this review are listed in
Summary of studies that predict medication adherence.
Study | Year | Data collection | Algorithms | Disease states | Strong predictors of adherencea | Adherence metric predicted | Main outcome metricb |
Walczak and Okuboyejo [ |
2017 | Self-reported questionnaire | ANNsc | General | Variable strength not discussed in detail | Self-reported reasons for medication adherence status | 79.3% accuracy when predicting reasons for poor adherence |
Son et al [ |
2010 | Self-reported questionnaire | SVMd | Heart failure | Disease severity classification, medication knowledge, gender, daily medication frequency, marital status | Self-reported medication adherence status | 77.6% accuracy when predicting medication adherence |
Aziz et al [ |
2020 | Self-reported questionnaire | ANNs, RFe, SVM | Hypertension | Education level, marital status, general overuse, monthly income, specific concern | Self-reported medication adherence status | 79% accuracy when predicting medication adherence |
Aznar-Lou et al [ |
2018 | Pharmacy claims data | LRf | General | Medication cost for low-income individuals | Filling a medication within 2 months of issuance | Statistically significant association between medication cost and medication adherence in low-income group |
Zhang and Meltzer [ |
2016 | Self-reported questionnaire | LR | General | Time between social security check arrival and filling prescriptions, age, specific chronic conditions | Self-reported adherence status | Statistically significant association between medication adherence and time difference between receiving social security check and filling drugs |
Aznar-Lou et al [ |
2017 | Pharmacy claims data | LR | High-cost diseases | Age, nationality, number of chronic conditions, active disease, being treated by primary care provider | Patient filling their medication within 1 month of issuance | Statistically significant association between medication adherence and age, nationality, number of chronic conditions, specific active diseases, type of care provider |
Haas et al [ |
2019 | Self-reported questionnaire | RF | Fibromyalgia | Type of medication, years of treatment, dosage, age, gender, region of residence | Self-reported adherence status on a health forum | 67.8% accuracy when predicting medication adherence |
Lu et al [ |
2005 | Self-reported questionnaire | ANNs, SVM | HIV | Viral load, drug abuse, alcohol abuse, psychiatric diagnosis, missed clinic visits, housing, HIV-related inpatient medical care | Self-reported adherence class ranging from class one to four | 100% accuracy when predicting adherence class but sample size of only 33 |
Franklin et al [ |
2016 | Pharmacy claims data | LR | High cholesterol | Age, sex, race, specific statin medication, count of health services score, cardiovascular disease status, cardiovascular procedure status, comorbidities, initial statin fill behavior | Proportion of days covered of 0.8 or higher | 84.2% accuracy when predicting medication adherence |
Karanasiou et al [ |
2016 | Clinical estimation | ANNs, SVM, RF, J48, random tree, logistic tree, classification tree, rotation forest, radial basis function, Bayesian network, Naive Bayes | Heart failure | Specific medical conditions, specific medications, medication dose, medication frequency | Provider determined adherence status to medications and lifestyle changes | 91% sensitivity when predicting medication adherence |
Bourdès et al [ |
2011 | Self-reported questionnaire | ANNs, LR | ACSg | Coronary artery bypass graft status, overweight with BMI between 25 and 30, hypercholesterolemia, education level | Self-reported concurrent use of an ace inhibitor or angiotensin receptor blocker, beta blocker, statin, and a blood thinning agent | 0.80 AUCh when predicting medication adherence |
Kim et al [ |
2019 | Self-reported questionnaire | Classification tree | Smoking cessation | Belief in medication safety, taste and sensory properties, exposure to others smoking, quitting confidence | Self-reported adherence during a phone interview | 2.22 importance score for belief in medication safety, 1.84 importance score for taste and sensory properties, 1.67 importance score for exposure to other smokers, 1.15 importance score for quitting confidence |
Kardas et al [ |
2020 | Pharmacy claims data | LR | Chronic disease | Age, medication cost, medication class | Not picking up a medication within 1 month of issuance | Statistically significant association found between age and medication adherence |
Desai et al [ |
2019 | Pharmacy claims data | LR | Fibromyalgia | Gender, age, race, comorbidity score, medication type, health coverage, emergency room visits | Medication possession ratio of 80% or higher | AUC of 0.6224 when predicting medication adherence |
Elahinia et al [ |
2017 | Discharge summaries | Natural language processing | General | Keywords frequently found in the discharge summaries of nonadherent patients | Adherence status based on professional interpretation of discharge summaries | Statistically significant association between category 1, 2, and 3 keywords with medication adherence |
aPredictor strength was based on individual study results.
bMain outcome metric based on judgement of the research team after careful consideration of all results presented in the individual study.
cANN: artificial neural network.
dSVM: support vector machine.
eRF: random forest.
fLR: logistic regression.
gACS: acute coronary syndrome.
hAUC: area under the curve.
Summary of studies that monitor medication adherence.
Study | Year | Algorithms | Sensors | Diseases | Data analyzed using machine learning to determine adherence | Main outcome metrica |
Hezarjaribi et al [ |
2016 | Classification tree | Body worn | Chronic disease | Smartwatch movement data collected during one of five tasks: drinking water, taking a pill while sitting, taking a pill while standing, writing, eating | 78.6% detection accuracy using only smartwatch sensors |
Fozoonmayeh et al [ |
2020 | RFb, LRc, SVMd, boosted tree | Body worn | General | Smartwatch movement data collected during medication ingestion or other predetermined tasks | 98.3% detection accuracy using smartwatch sensors |
Aldeer et al [ |
2019 | RF, SVM | Smart pill bottle | General | Medication bottle movement and cap sensor data collected during medication ingestion or other predetermined tasks | 90% accuracy when using all smart bottle sensors |
Ntalianis et al [ |
2019 | ANNse | Audio recording device | Asthma/COPDf | Sound recordings of inhaler use broken down into different inhaler use actions like breathing in, actuation, and breathing out | 95.86% classification accuracy using audio files |
Tucker et al [ |
2015 | NBg, IBkh, SVM, J48, RF | Kinect | Parkinson disease | 3D movement scans of patients with Parkinson disease in different adherence states | 97% classification accuracy when determining medication status for a single patient, 78% classification accuracy when determining medication status of multiple patients |
Ma et al [ |
2018 | RF | Body worn | General | Movement data collected using a smartwatch during six predefined activities: medication intake with nondominant hand, medication intake with dominant hand, walking, texting, writing with a pen, drinking water | Recall of 1.00 with a precision of 0.80 for medication intake classification using smartwatch sensors |
Nousias et al [ |
2016 | SVM, RF, AdaBoost | Audio recording device | Asthma/COPD | Audio recordings of inhaler use, breathing in, breathing out, and noise not related to medication use | 97.59% classification accuracy using audio recordings |
Bilodeau and Ammouri [ |
2011 | Petri net | Camera | General | Video footage of people taking medications in which the head, hands, and medication bottle are clearly visible | Correctly identified medication taking 9 out of 12 times |
Aldeer et al [ |
2019 | SVM, RF | Smart pill bottle | General | Patient-specific movement profile data collected using pill bottle movement and cap sensors | 91% accuracy identifying the movement patterns of a specific patient |
Zhang et al [ |
2019 | ANNs | Body worn | Parkinson disease | Movement data of patients with Parkinson disease collected using a smartphone | 83.4% classification accuracy using body worn sensors |
Pettas et al [ |
2019 | ANNs, RF | Audio recording device | Asthma/COPD | Audio recordings of sounds related to inhaler use or recordings of non-inhaler–related sounds | Classification accuracy of 93.75% using audio recordings |
Moldovan et al [ |
2018 | ANNs, LR, RF, decision tree | Body worn | Dementia | Movement data of patients with dementia during medication ingestion and other predetermined tasks collected using four body worn motion sensors | Classification precision of 0.91 using body worn sensors |
Kikidis et al [ |
2015 | ANNs | Audio recording device | Asthma/COPD | Audio recordings that contain either an inhaler actuation or a non-inhaler–related sound | 99.5% classification accuracy using audio recordings |
aMain outcome metric based on judgement of the research team after careful consideration of all results presented in the individual study.
bRF: random forest.
cLR: logistic regression.
dSVM: support vector machine.
eANN: artificial neural network.
fCOPD: chronic obstructive pulmonary disease.
gNB: Naive Bayes.
hIBk: instance-based classifier with parameter k.
Summary of studies that monitor and attempt to improve medication adherence.
Study | Year | Sensors | Adherence intervention | Disease states | Algorithms | Data analyzed using machine learning to determine adherence | Main outcome metrica |
da Silva et al [ |
2019 | Smart medication cabinet, cameras, movement sensors, light sensors, thermostat, smart TV sensors, smartphone sensors, smartwatch sensors, door sensors, blood pressure sensors | Context-aware medication reminder prompts | Hypertension | C4.5, random tree, RepTree | Medication ingestion confirmed by medication cabinet using door sensors, RFIDb tags, video recording, patient daily patterns monitored using motion sensors in house, light sensors, thermometer sensors, smart TV sensors, smartphone sensors, home door sensors, blood pressure sensors | 95.10% medication adherence level for people using the system |
Lundell et al [ |
2007 | Smart medication cabinet, motion sensors, refrigerator sensors, smartphone sensors, smartwatch sensors, bed sensors, front door sensors | Context-aware medication reminder prompts | General | Dynamic Bayesian network | Medication ingestion confirmed using pill tray lid sensors, patient daily patterns tracked using motion sensors in house, refrigerator sensors, smartphone sensors, smartwatch sensors, bed sensors, front door sensors | 92% medication adherence for people using the system |
Silva et al [ |
2018 | Smart medication cabinet, computer sensors, tablets sensors, TV sensors, smartwatch sensors, smartphone sensors | Context-aware medication reminder prompts | Hypertension | J48, RFc, RepTree, random tree | Medication ingestion determined by smart drug cabinet that uses RFID tags and door sensors, patient daily patterns tracked using computer sensors, tablet sensors, smart TV sensors, smartphone sensors, smartwatch sensors | 95.2% classification accuracy using all available sensors |
Silva et al [ |
2016 | Smart medication cabinet, motion sensors, lighting sensors, cameras, smartphone sensors, TV sensors | Context-aware medication reminder prompts | General | J48, RepTree, random tree | Medication ingestion determined using smart medication cabinet with RFID tags and camera system, patient daily activity tracked using motion sensors in home, light sensors, surveillance cameras, smartphone sensors, smart TV sensors | Testing of a prototype system was conducted but no statistical results were presented |
Nousias et al [ |
2018 | Microphone | Visualization of inhaler use process | Asthma, COPDd | GMMe, SVMf, RF, AdaBoost | Audio recordings of inhalation, exhalation, inhaler actuation, and background noise | 98% classification accuracy using audio recordings |
Persell et al [ |
2020 | None | Conversational AIg adherence coaching | Hypertension | Not discussed in detail | Blood pressure, weight, self-reported adherence, number of medications, number of dose increases or substitutions, compliance with: diet, exercise, sleep duration | No significant differences in adherence when comparing patients using smartphone coaching app vs those not using the app |
Brar Prayaga et al [ |
2018 | None | Conversational AI refill reminder system | Chronic disease | Not discussed in detail | Medication names, gender, number of refills processed using the system, patient responses using keypad or unstructured verbal responses | Text messaging reminder system improved adherence significantly with 14.07% more refills than the control group receiving traditional reminders |
Chaix et al [ |
2019 | None | Conversational AI medication reminder system | Breast cancer | Not discussed in detail | Patient verbal responses to medication reminder: “yes I took it,” “no i didn’t take it,” “send me a message in 15 minutes” | Average compliance improved significantly in the chatbot group with 20% higher adherence levels when compared to the control group |
Curci et al [ |
2017 | Smart pill bottle | Medication reminder, nonadherence messaging to provider | General | RF, RIPPER, Bayesian networks, SVM, ANNsh | Movement sensor data recorded by pill bottle, patient response via cell phone app answering if they took the medication when medication ingestion is suggested by movement data, patient response to scheduled reminders indicating if the medication was taken or not | 90% classification accuracy using medication bottle movement sensors |
Labovitz et al [ |
2017 | Camera | Medication reminder, adherence history visualization | Ischemic stroke | Not discussed in detail | Video recordings of medication ingestion using a smartphone and pill counts for patients in group one, blood concentration of medication and pill counts for patients in group two | Patient adherence using the AI platform was 90.5% compared with 100% using blood samples to measure drug levels |
aMain outcome metric based on judgement of the research team after careful consideration of all results presented in the individual study.
bRFID: radio-frequency identification.
cRF: random forest.
dCOPD: chronic obstructive pulmonary disease.
eGMM: Gaussian mixture model.
fSVM: support vector machine.
gAI: artificial intelligence.
hANN: artificial neural network.
Before dividing the studies into categories, they were examined as a whole to determine the distribution of articles concerning time, disease states, and database type.
Number of included studies per year.
Year of publication | Studies included, n |
2005 | 1 |
2007 | 1 |
2010 | 1 |
2011 | 2 |
2013 | 1 |
2015 | 2 |
2016 | 6 |
2017 | 7 |
2018 | 6 |
2019 | 12 |
2020 | 4 |
Number of included studies per disease group.
Disease groups | Studies included, n |
Nonspecific | 12 |
Cardiovascular diseases | 10 |
Pulmonary diseases | 5 |
General chronic diseases | 4 |
Diseases of aging | 3 |
Psychiatric diseases | 2 |
Infectious diseases | 2 |
Chronic pain | 2 |
Smoking cessation | 1 |
Cancer | 1 |
Diseases with expensive medications | 1 |
Following the general analysis, actions related to medication adherence were determined. The three identified actions were prediction of adherence, adherence monitoring, and adherence interventions. Next, studies were grouped into natural categories for further analysis according to the combination of medication adherence actions that they contain. The following natural groups were identified: prediction of adherence only, monitoring of adherence only, monitoring with an intervention to improve adherence, all three medication adherence actions simultaneously, and prediction with monitoring.
The fourth group contained 3 articles that predicted adherence, monitored adherence, and intervened to improve medication adherence [
The first of these studies allowed patients to request medication refills using a conversational AI SMS text messaging system [
The next work used data from the 99DOTS (Directly Observed Treatment Short Course) program, which monitors medication adherence of patients with tuberculosis [
The next paper used face recognition software and computer vision to monitor medication adherence of 53 patients that had schizophrenia [
The next group had 2 articles that predicted and monitored medication adherence but did not introduce any medication adherence intervention [
The first of these articles used data collected during hospital stays to generate predictors. Of these predictors, disease severity and biomarkers (breath, saliva, blood) had the largest impact on the model’s accuracy [
The second paper used interactive voice response assessments to predict future adherence and to monitor current medication adherence in patients with depression [
This is the first review to focus broadly on applying machine learning to medication adherence. This study provides a general summary of the topic and categorizes literature according to the combination of medication adherence actions. Within each category, common themes were explored and opportunities for future work were identified.
The application of machine learning to medication adherence is a topic still in its infancy that has become more prevalent over the last few years. This technology is typically being applied to patients with chronic diseases that require long-term medication use. In fact, 29 of the 43 studies looked at using machine learning to impact medication adherence within the context of chronic disease [
Twenty predictors of medication adherence were found to be important across two or more independent studies [
Similar methods of data collection were also used in works that solely predicted medication adherence. The most popular method of data collection and determining adherence was self-reported questionnaires. In fact, 8 of the 15 studies in this group determined medication adherence using self-reported questionnaires [
Additionally, studies that only predicted medication adherence used many of the same algorithms. Of 15 studies, 13 in this group used either logistic regression, artificial neural network, SVM, or random forest algorithms. Some of these works compared the different types of algorithms to determine which was the most accurate [
Furthermore, 13 of the 15 studies that attempted to predict medication adherence used either a self-reported adherence metric or medication filling data to train their models [
Machine learning was also used to monitor medication adherence. The main purpose of all 13 studies within this group was to develop new ways of monitoring medication adherence with the aid of sensors and machine learning. One popular approach, used in 6 of these 13 studies, was to attach sensors to the container holding the patient’s medication [
Another popular approach was to use portable smart devices to track medication adherence. Of these studies, 6 used portable sensors found in common consumer electronics like smartwatches or smartphones [
Studies that only monitored medication adherence used many of the same machine learning algorithms. Of the 13 studies, 11 in this group used logistic regression, artificial neural network, SVM, or random forest algorithms [
Moreover, works that only monitored medication adherence often built systems that classified user activities based on data provided by sensors. Several studies attempted to monitor the use of inhalers by analyzing audio recordings of inhaler use [
Two other studies observed the movement patterns of patients with Parkinson disease [
A total of 10 studies also monitored medication adherence and introduced an intervention to improve it [
Machine learning also has the potential to substantially improve medication adherence. However, the development of this technology must be well guided to ensure optimal outcomes. The information presented in this review indicates that some predictors remain significant across multiple studies. The creation of more generalizable models that can be quickly adapted to new data sets may make prediction of medication adherence a less time-consuming endeavor in the future. Higher quality measures of adherence status should also be adopted, when possible, to ensure that predictions are based on accurate data. Currently, machine learning has the ability to predict medication adherence with a good level of accuracy. These predictions should be paired with targeted interventions to help prevent medication adherence issues before they occur. However, careful evaluation of models is still paramount to avoid wasting resources on systems that overpredict medication nonadherence. More work also needs to be done to identify predictors of medication adherence in free-text documents. Currently, a lot of medical data is in a free-text format, and this is especially the case for less advanced systems with less structured documentation.
Moreover, systems that monitor medication adherence can accurately classify inhaler use actions, even in the presence of background noise. This technology can be used to track inhaler adherence but can also be taken a step further, allowing for directed interventions aimed at improving inhaler use technique. Adherence monitoring systems are also currently capable of accurately determining the medication status of patients with Parkinson disease using movement data. This information could be used beyond mere adherence by providing clinicians with information that could be used to guide dosage adjustments. This is particularly helpful in a population that is likely to have difficulty communicating their struggles to providers. Systems are also being developed to track medication administration using only smartwatch movement sensors. However, these systems are still in the early development phase and are only being asked to differentiate between a small handful of different activities. These systems need to maintain a good level of classification accuracy in real-world environments before they can offer any clinical utility. However, if these systems are able to achieve this feat they will be highly attractive since they allow for unobtrusive monitoring with common devices that many patients already wear.
Context-aware reminder systems have shown that they can help patients to achieve a high level of adherence but do so in an intrusive fashion. Studies need to be conducted to evaluate the acceptability and desirability of systems like this. These systems should also be compared against traditional reminder systems to make sure they are actually improving adherence. Conversational AI systems aimed at improving medication adherence are already starting to be deployed, and a few have significantly improved adherence over traditional reminder systems. One advantage of these systems is that people can interact with them using the spoken word, so they may be more usable for people who have difficulty interacting with systems requiring the ability to use a computer or smartphone.
The analysis of this topic was limited to five important databases, and some relevant articles from other sources may have been missed. Different grouping strategies might have also added additional insights but were not attempted since they are outside the scope of this paper.
Supplementary material.
artificial intelligence
Laboratory of Applied Informatics Research
Medical Subject Headings
Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews
support vector machine
Directly Observed Treatment Short Course
The funding that made writing this paper possible was provided by the National Library of Medicine as part of a T-15 research fellowship in clinical informatics.
None declared.