Extracting Adherence Information from Electronic Health Records (2020)

Abstract: Patient adherence is a critical factor in health outcomes. We present a framework to extract adherence information from electronic health records, including both sentence-level information indicating general adherence information (full, partial, none, etc.) and span-level information providing additional information such as adherence type (medication or nonmedication), reasons and outcomes. We annotate and make publicly available a new corpus of 3,000 de-identified sentences, and discuss the language physicians use to document adherence information. We also explore models based on state-of-the-art transformers to automate both tasks.

## introduction patient adherence, also known as compliance, is the degree to which a patient follows medical advice. adherence includes not only taking medications as prescribed, but also using medical devices as instructed, following diet and exercise recommendations, etc. as we shall see, adherence is a critical factor in health outcomes, particularly in patients with chronic illnesses. patients play the central role following medical advice and communicating adherence to health providers: they largely self-administer, self-regulate and self-report. while insurance claims and filling prescriptions on time may be available as structured data, these sources of information only provide a partial view of patient adherence. indeed, not going to a specialist or not filling a prescription are sufficient to detect some forms of non-adherence, but doing so is insufficient to guarantee adherence. additionally, many other forms of non-adherence are common (e.g., skipping medications, taking the wrong dosage, following a diet only occasionally). from a computational perspective-including both annotation and model building efforts-patient adherence has been primarily modeled as a binary decision, with an emphasis on pinpointing nonadherence. adherence, however, ranges from absolute non-adherence to full adherence. partial adherence and non-adherence can be due to many factors. adherence models by medical experts include intentional and unintentional non-adherence @xcite , social support @xcite and other patient attributes such as age and time since diagnosis @xcite . in this paper, we extract medication adherence information from electronic health records. natural language processing is a requirement to solve this problem, as physicians record self-reported patient adherence (or non-adherence) in unstructured free text. we identify not only whether a patient has adhered fully or not at all to medical advice, but also partial adherence as well as doctors reviewing or instructing patients about treatments and the importance of future adherence. in addition, we target for the first time information to better understand adherence. this information includes adherence type (medication or non-medication), the source of adherence information (the patient, a care giver, etc.), reason of non-adherence (cost, intolerance, forgetfulness, etc.), speculation (doesn't remember, is not sure, etc.) and negation (forgot, stopped, etc.) and others. the main contributions of this paper are: 1 ## background and previous work electronic health records have emerged in the last decade as a standard in healthcare @xcite . studies have shown that they are beneficial both in small practices and large organizations @xcite . while electronic health records are notoriously difficult to annotate and mine for information @xcite , modern machine learning and in particular deep learning has seen many successes @xcite . some of the remaining challenges include the lack of a universal standard and the inherent difficulties of working in the medical domain (abbreviations, jargon, incomplete sentences, etc.) the consequences of non-adherence. among individuals with chronic illnesses, medication nonadherence is an important contributor to (a) poor patient outcomes related to increased morbidity and mortality, and (b) increased healthcare costs related to avoidable hospitalizations and emergency department visits @xcite @xcite . non-adherence is associated with @xmath0300 billion of avoidable health care costs (3-10% of the total) in the us annually @xcite . despite these facts, studies have shown that up to 50% of patients in the us with chronic illnesses stop taking their medications within one year of being prescribed @xcite , and over 31% of prescriptions are not filled within 9 months @xcite . adherence and electronic health records. previous work has measured medication adherence objectively using medication event monitoring systems @xcite . to our knowledge, the work by @xcite is the first to extract adherence from physician notes. their system, however, is based on 87 heuristics using pattern matching, and their evaluation considers only 82 notes. physician-patient transcripts have been studied as a source of information to predict non-adherence. for example, @xcite conclude that unigrams in psychiatrist-patient transcripts are good predictors of future adherence to treatment for schizophrenia (as determined by the physician), and @xcite investigate the role of topics automatically identified. more recently and still using traditional machine learning, @xcite automatically identify whether utterances discuss adherence barriers. similar to our sentence-level annotations, all these previous works consider adherence as a text classification task. that is, they assign one label to a piece of text (e.g., a full dialogue transcript, sentence, utterance). unlike them, we also consider span-level adherence information including adherence type (medication or non-medication), the reasons and outcomes for adhering (or not adhering), etc. social media has also been studied as a source of adherence information, @xcite find that out of 400 tweets mentioning drugs, 9 tweets indicate non-adherence and 6 include the reason for not adhering (e.g., adverse effects). they do not, however, attempt to automatically extract any adherence information. @xcite present a corpus (1,102 discharge summaries and 1,000 nursing progress notes) annotated with 13 patient phenotypes, one of which is non-adherence. they also present results with a cnn, but do not report results identifying non-adherence. their definition of non-adherence is equivalent to our partial and none adherence (section 3), and like the other works described above, they reduce the problem to text classification and disregard span-level information. deep learning approaches have been proven useful to extract information from electronic health records beyond adherence. for example, @xcite show that bilstm networks are useful for medical event detection, and @xcite experiment with transfer learning, grus and bert to identify sections (e.g., allergies, chief complaint, examination, family history, procedures, etc.) in electronic health records. ## annotating adherence large corpora of electronic health records are not publicly available because of privacy concerns thus we work with new sentences. we summarize below the final annotation guidelines. the guidelines were refined after several iterations with pilot annotations and discussions with the annotators. ## experiments and results we create stratified train, development, and test splits (75/5/20) and experiment with transformers to automate both annotation tasks. more specifically, we work with four versions of bert @xcite . three versions are pre-trained with general english: the base and large bert models (110m and 340m parameters) and a smaller version (65m parameters) shown to be as effective, distillbert @xcite . the fourth transformer, clinicalbert @xcite , is pretrained in the clinical domain. we use the transformers package by @xcite , keras (chollet and others, 2015), and ktrain (maiya, 2020) to tune the models with the train and development splits. ## error analysis we conduct a manual error analysis of a sample of 100 sentences from the test set in order to identify the most commons error types made by the best models in each task. we identified well-known linguistic phenomena that are challenging in any domain as well as specific issues present in electronic health records. our future plans include improving the models based on these insights.