Apoorva Milind Pradhan

Introduction: Recreational and medical cannabis use (CU) information is often available within the electronic health record (EHR) in a format that is impractical for health care provider use. Transformation of free-text EHR documentation in notes to discrete elements is possible using natural language processing (NLP) and has the potential to characterize CU efficiently. The objective of this study was to develop an NLP algorithm to identify documentation of CU within EHR unstructured clinical notes. Methods: We identified EHR notes with cannabis-related terminologies through a keyword search among all Geisinger patients with at least one encounter between 1/1/2013 and 6/30/2022. We trained four NLP models to classify notes into six categories based on time, context, and reliability of CU documentation identified through manual annotation. We compared the demographic characteristics of patients with positive classification for CU using the best-performing model to those of the overall population. Results: Of the over 1.7 million eligible patients, 150,726 (8.6%) were flagged as cannabis users. The Bio-ClinicalBERT, a transformer-based NLP model, achieved close to human performance in classifying CU (weighted Precision=91.4, Recall=93.3, F-score=92.4). Cannabis users had higher BMI and were at least nine-fold more likely to use tobacco, alcohol, and illicit substances. Conclusion: Our study evaluated the prevalence of CU documentation across the entire corpus of EHR notes data without population segmentation. The NLP methodologies used achieved performance close to that of human annotation and laid the foundation for identifying and classifying CU within unstructured data sources, with future applications in research and patient care. Plain Language Summary: Marijuana, also known as cannabis, may impact the health of patients, yet it is not routinely captured in medical records, and when documented, it is often found in unstructured formats (e.g., progress notes) rather than in discrete fields. Incomplete and unstructured capture limits many functional capabilities within the EHR that enhance patient care (e.g., drug interactions, notifications) and limit researchers from identifying patients routinely exposed to marijuana use. The transformation of free-text documentation of cannabis use (CU) into discrete elements can be performed using natural language processing (NLP). The objective of this study was to develop an NLP model to identify CU in unstructured clinical notes in the EHR. We examined the EHRs of Geisinger patients in Pennsylvania over a 10-year period. Among 1.7 million patients, 9% were identified as CU. One of the NLP models tested, Bio-ClinicalBERT, achieved the highest performance. Cannabis users had a higher BMI and were ten-fold more likely to be tobacco users, ten-fold more likely to use alcohol, and nine-fold more likely to use illicit substances. NLP can be used to better understand the risks and benefits of CU at a population level and may improve patient identification to assist clinical decision-making. Future CU epidemiological research should continue to explore other avenues to automate and improve CU documentation by leveraging rapidly evolving technologies, such as artificial intelligence-driven tools.

Purpose: Ambient Documentation Tools (ADT) are an emerging technology designed to help clinicians complete documentation better with less time and effort. This study aimed to understand the impact of ADT on the pharmacist care experience. Methods: Data from Epic Signal, surveys, and interviews were collected between February 2024 and October 2025 from 41 medication therapy disease management (MTDM) pharmacists across 33 primary care and subspecialty clinics at a large integrated health system who were given ADT licenses. Study variables included pharmacist ADT utilization rate and pre-/post-changes in time spent in notes per encounter, as well as perceptions of documentation burden, patient access, undivided attention for patients, afterhours documentation, and burnout. Quantitative data were analyzed using descriptive statistics, regression models, and comparative tests of pre-/post-statistical significance and effect size. Qualitative data were mined for exemplary excerpts to deepen understanding. Results: Thirty pharmacists from 28 clinics utilized ADT and provided usable responses. ADT was utilized for 65% of eligible encounters, and the average time in notes per encounter fell 86 seconds post-ADT. Pharmacist perceptions of documentation burden, undivided attention ability, and afterhours documentation improved post-ADT. Interview responses were largely positive for most variables and revealed multiple explanatory mechanisms. Conclusion: ADT meaningfully improved several care experience aspects for MTDM pharmacists over a short period of time (2-7 months). Future research with larger samples and longer time horizons across multiple health systems is needed to investigate the full and sustained impact of ADT on the care experience. Key points: This mixed-methods study is the first to investigate the impact of an ambient documentation tool (ADT) on the care experience of clinical pharmacists embedded in ambulatory clinics. Data from Epic Signal, surveys, and interviews were triangulated to assess ADT usage and evaluate pre-/post-ADT deployment changes in pharmacist care activities and perceptions. Pharmacists activated ADT for 65% of eligible encounters, reducing time spent in notes by 86 seconds per encounter and improving their perceptions of documentation burden, ability to give patients undivided attention, and afterhours documentation.

Background and objectives: Primary headache disorders, particularly migraine, pose a significant public health challenge in the United States, marked by emergency department (ED) overuse, poor outcomes, and rising costs. Despite their utility in understanding disability, semistructured interviews are underutilized for identifying areas of improvement in system-based care. This project sought to capture patient perspectives on optimizing care for chronic headache patients with refractory symptoms. Methods: Semistructured interviews were conducted with refractory headache patients in Northeast and Central Pennsylvania who had frequent ED visits, frequent abortive medication refills, or were referred to Neurology without adequate medication trials. An interview guide exploring aspects of headache care was developed. Transcripts underwent coding and collaborative review for inductive thematic analysis. Results: Sixteen interviews of predominantly middle-aged, unemployed or disabled patients with an average of 20 monthly headache days were conducted. Analysis revealed five themes for system-based care: convenience, empowerment, continuity, timeliness, and acceptance. Convenience: Over 50% reported negative ED experiences due to prior ambulatory care challenges including issues with refills, prior authorizations, and scheduling. Telemedicine and patient portals improved access and reduced burdens. Empowerment: Patients struggled with understanding and managing their condition; 38% expressed fatalism about treatment. Tools like symptom trackers and structured education were valued but underused. Continuity: Social, economic, and logistical barriers hindered follow-up, with 50% citing transportation and financial issues. Limited access to subspecialists led to ED reliance. Timeliness: Delays in care, diagnostic uncertainty, and failed medication trials were common. Nonheadache comorbidities and rigid care algorithms further impeded timely treatment. Acceptance: Stigma, especially in acute settings, discouraged care-seeking, with 50% being perceived as drug-seeking. Positive provider interactions fostered trust and tailored care. Discussion: Recommendations include emphasizing telemedicine, streamlined prior authorizations, and automated refills to improve access. Structured educational resources, headache tracking tools, and support groups can enhance patient engagement. Regular follow-ups using pharmacist-led medication management can improve therapeutic consistency. Early diagnosis and treatment through modern triage systems can prevent delays and long-term complications. Reducing stigma via personalized care plans and system-wide programs fosters trust within the headache community. These findings offer a framework for quality improvement in headache care across diverse healthcare systems.

Background: Medical marijuana (MMJ) is available in Pennsylvania, and participation in the state-regulated program requires patient registration and receiving certification by an approved physician. Currently, no integration of MMJ certification data with health records exists in Pennsylvania that would allow clinicians to rapidly identify patients using MMJ, as exists with other scheduled drugs. This absence of a formal data sharing structure necessitates tools aiding in consistent documentation practices to enable comprehensive patient care. Customized smart data elements (SDEs) were made available to clinicians at an integrated health system, Geisinger, following MMJ legalization in Pennsylvania. Objective: The purpose of this project was to examine and contextualize the use of MMJ SDEs in the Geisinger population. We accomplished this goal by developing a systematic protocol for review of medical records and creating a tool that resulted in consistent human data extraction. Methods: We developed a protocol for reviewing medical records for extracting MMJ-related information. The protocol was developed between August and December of 2022 and focused on a patient group that received one of several MMJ SDEs between January 25, 2019, and May 26, 2022. Characteristics were first identified on a pilot sample (n=5), which were then iteratively reviewed to optimize for consistency. Following the pilot, 2 reviewers were assigned 200 randomly selected patients’ medical records, with a third reviewer examining a subsample (n=30) to determine reliability. We then summarized the clinician- and patient-level features from 156 medical records with a table-format SDE that best captured MMJ information. Results: We found the review protocol for medical records was feasible for those with minimal medical background to complete, with high interrater reliability (κ=0.966; P<.001; odds ratio 0.97, 95% CI 0.954-0.978). MMJ certification was largely documented by nurses and medical assistants (n=138, 88.5%) and typically within primary care settings (n=107, 68.6%). The SDE has 6 preset field prompts with heterogeneous documentation completion rates, including certifying conditions (n=146, 93.6%), product (n=145, 92.9%), authorized dispensary (n=137, 87.8%), active ingredient (n=130, 83.3%), certifying provider (n=96, 61.5%), and dosage (n=48, 30.8%). We found preset fields were overall well-recorded (mean 76.6%, SD 23.7% across all fields). Primary diagnostic codes recorded at documentation encounters varied, with the most frequent being routine examinations and testing (n=34, 21.8%), musculoskeletal or nervous conditions, and signs and symptoms not classified elsewhere (n=21, 13.5%). Conclusions: This method of reviewing medical records yields high-quality data extraction that can serve as a model for other health record inquiries. Our evaluation showed relatively high completeness of SDE fields, primarily by clinical staff responsible for rooming patients, with an overview of conditions under which MMJ is documented. Improving the adoption and fidelity of SDE data collection may present a valuable data source for future research on patient MMJ use, treatment efficacy, and outcomes.

Background: Patient portal secure messaging allows patients to describe health-related behaviors in ways that may not be sufficiently captured in standard electronic health record (EHR) documentation, but little is known about how cannabis is discussed on this platform. Objective: This study aimed to identify patient and provider secure messages that discussed cannabis and contextualize these discussions over periods before and after its legalization for medical purposes in Pennsylvania. Methods: We examined 382,982 secure messages sent by 15,340 patients and 6101 providers from an integrated health delivery system in Pennsylvania, United States, from January 2012 to June 2022. We used an unsupervised natural language processing approach to construct a lexicon that identified messages explicitly discussing cannabis. We then conducted a qualitative content analysis on a random sample of identified messages to understand the medical reasons behind patients’ use, the primary purposes of the cannabis-related discussions, and changes in these purposes over time. Results: We identified 1782 messages sent by 1098 patients (7.2% of total patients in the study) and 800 messages sent by 430 providers (7% of total providers in the study) as explicitly discussing cannabis. The most common medical reasons for use stated by patients in 190 sampled messages included pain or a pain-related condition (50.5% of messages), anxiety (13.7% of messages), and sleep (11.1% of messages). We coded 56 different purposes behind the mentions of cannabis in patient messages and 33 purposes in 100 sampled provider messages. In years before the legalization (2012-2016), patient and provider messages (n=20 for both) were primarily driven by discussions about cannabis screening results (38.9% and 76.5% of messages, respectively). In the years following legalization (2017-2022), patient messages (n=170) primarily involved seeking assistance to facilitate medical use (35.2% of messages) and reporting current use (25.3% of messages). Provider messages (n=80) were driven by giving assistance with medical marijuana access (27.5% of messages) and stating that they were unable to refer, prescribe or recommend medical marijuana (26.3% of messages). Conclusions: Patients showed a willingness to discuss cannabis use over patient portal secure messages and expressed interest in use after the legalization of medical marijuana. Some providers responded to patient inquiries with assistance in obtaining access to medical marijuana, while others cautioned patients on the risks of use. Insight into cannabis-related discussions through secure messages can help health systems determine opportunities to improve care processes around patients’ cannabis use, and providers should be supported to communicate accurate and consistent information.

BACKGROUND Medical Marijuana (MMJ) is available in Pennsylvania and participation in the state-regulated program requires a patient to register and receive a certification by an approved physician. There is currently no integration of MMJ certification data in Pennsylvania into health records that would allow for physicians to rapidly identify patients that are using MMJ, as there are with other scheduled drugs. This absence of a formal data sharing structure necessitates tools that aid in consistent documentation practices to enable comprehensive patient care. OBJECTIVE Customized smart data elements (SDE) were made available to clinicians at an integrated health system, Geisinger, following MMJ legalization in Pennsylvania. The purpose of this project was to examine and contextualize the use of MMJ SDEs in the Geisinger population. We accomplished this goal by developing a systematic chart review protocol, with the goal of creating a tool that resulted in consistent human data extraction. METHODS We developed a chart review protocol for extracting MMJ-related information. The protocol was developed between August to December of 2022 and focused on a patient group that received one of several MMJ SDE between 1/25/2019 and 5/26/2022. Characteristics were first identified on a small pilot sample of patients (N=5), which were then iteratively reviewed to optimize for consistency. Following the pilot, two reviewers were assigned 200 patient charts, selected randomly from the larger cohort, with a third reviewer examining a subsample to determine reliability. We then summarized the clinician-level and patient-level features from n=156 charts with a table-format SDE that best captured MMJ information. RESULTS We found the chart review protocol was feasible for those with minimal medical background to complete, with high inter-rater reliability (Kappa = 0.966 (p <0.001), 95% CI (0.954 - 0.978)). MMJ certification was largely documented by nurses and medical assistants (87.2%) and typically within primary care settings (68.6%). The SDE has 6 pre-set field prompts, including certifying provider, authorized dispensary, certifying conditions, dosage, product, and active ingredient. We found preset fields were overall well-recorded (76.6% across all fields). Individual fields were more heterogeneous in terms of completion, with dispensary specified in 87.8% of documentation, certifying provider specified in 61.5% of documentation, and product dose specified in only 30.8% of documentation. CONCLUSIONS This method of chart review yields high quality data extraction that can serve as a model for other health record inquiries. Our evaluation showed relatively high completeness of SDE fields, primarily by clinical staff responsible for rooming patients. Improving adoption and fidelity of SDE data collection may present a valuable data source for future research on patient MMJ use and treatment efficacy and outcomes. CLINICALTRIAL N/A

Background: Headaches, including migraines, are one of the most common causes of disability and account for nearly 20%‐30% of referrals from primary care to neurology. In primary care, electronic health record–based alerts offer a mechanism to influence health care provider behaviors, manage neurology referrals, and optimize headache care. Objective: This project aimed to evaluate the impact of an electronic alert implemented in primary care on patients’ overall headache management. Methods: We conducted a stratified cluster-randomized study across 38 primary care clinic sites between December 2021 to December 2022 at a large integrated health care delivery system in the United States. Clinics were stratified into 6 blocks based on region and patient-to–health care provider ratios and then 1:1 randomized within each block into either the control or intervention. Health care providers practicing at intervention clinics received an interruptive alert in the electronic health record. The primary end point was a change in headache burden, measured using the Headache Impact Test 6 scale, from baseline to 6 months. Secondary outcomes included changes in headache frequency and intensity, access to care, and resource use. We analyzed the difference-in-differences between the arms at follow-up at the individual patient level. Results: We enrolled 203 adult patients with a confirmed headache diagnosis. At baseline, the average Headache Impact Test 6 scores in each arm were not significantly different (intervention: mean 63, SD 6.9; control: mean 61.8, SD 6.6; P=.21). We observed a significant reduction in the headache burden only in the intervention arm at follow-up (3.5 points; P=.009). The reduction in the headache burden was not statistically different between groups (difference-in-differences estimate –1.89, 95% CI –5 to 1.31; P=.25). Similarly, secondary outcomes were not significantly different between groups. Only 11.32% (303/2677) of alerts were acted upon. Conclusions: The use of an interruptive electronic alert did not significantly improve headache outcomes. Low use of alerts by health care providers prompts future alterations of the alert and exploration of alternative approaches.

BACKGROUND Patient portal secure messaging allows patients to describe health-related behaviors in ways that may not be sufficiently captured in standard electronic health record (EHR) documentation, but little is known about how cannabis is discussed on this platform. OBJECTIVE This study aimed to identify patient and provider secure messages that discussed cannabis and contextualize these discussions over periods before and after its legalization for medical purposes in Pennsylvania. METHODS We examined 382,982 secure messages sent by 15,340 patients and 6101 providers from an integrated health delivery system in Pennsylvania, United States, from January 2012 to June 2022. We used an unsupervised natural language processing approach to construct a lexicon that identified messages explicitly discussing cannabis. We then conducted a qualitative content analysis on a random sample of identified messages to understand the medical reasons behind patients’ use, the primary purposes of the cannabis-related discussions, and changes in these purposes over time. RESULTS We identified 1782 messages sent by 1098 patients (7.2% of total patients in the study) and 800 messages sent by 430 providers (7% of total providers in the study) as explicitly discussing cannabis. The most common medical reasons for use stated by patients in 190 sampled messages included pain or a pain-related condition (50.5% of messages), anxiety (13.7% of messages), and sleep (11.1% of messages). We coded 56 different purposes behind the mentions of cannabis in patient messages and 33 purposes in 100 sampled provider messages. In years before the legalization (2012-2016), patient and provider messages (n=20 for both) were primarily driven by discussions about cannabis screening results (38.9% and 76.5% of messages, respectively). In the years following legalization (2017-2022), patient messages (n=170) primarily involved seeking assistance to facilitate medical use (35.2% of messages) and reporting current use (25.3% of messages). Provider messages (n=80) were driven by giving assistance with medical marijuana access (27.5% of messages) and stating that they were unable to refer, prescribe or recommend medical marijuana (26.3% of messages). CONCLUSIONS Patients showed a willingness to discuss cannabis use over patient portal secure messages and expressed interest in use after the legalization of medical marijuana. Some providers responded to patient inquiries with assistance in obtaining access to medical marijuana, while others cautioned patients on the risks of use. Insight into cannabis-related discussions through secure messages can help health systems determine opportunities to improve care processes around patients’ cannabis use, and providers should be supported to communicate accurate and consistent information.

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