Record Linkage With Washington State Cancer Registry

Background/Aims: At Group Health, the virtual data warehouse’s (VDW) tumor registry table consists of tumor cases ascertained by Cancer Surveillance System (CSS) at the Fred Hutchinson Cancer Research Center (FHCRC). This data is limited to residents of 13 Puget Sound counties in the SEER region only. On the other hand, Washington State Cancer Registry (WSCR) consists of tumor cases for the entire Washington State residents. The poster describes the data linkage protocol and the results of linkage of Group Health members with WSCR database in the absence of unique identifier. Methods: Group Health data consists of 1,420,334 members residing in Washington State. We linked Group Health data with WSCR database consisting of 613,631 cancer cases diagnosed during January 1, 1993–December 31, 2011. We used a probabilistic linkage software application such as CDC’s Registry Plus Link Plus® to link patient information such social security number, date of birth, gender, and last and first names with corresponding data elements in WSCR. We assessed the quality of the linkage results by comparing the linkage outcome with VDW’s tumor registry. We calculated the sensitivity and positive predictive value by comparing the WSCR linkage results with VDW’s tumor registry. We used Venn diagrams for visualization of results of comparison. Results: Sensitivity and positive predictive value are 90% and 96%, respectively. We found 35,166 (59%) additional tumor cases as a result of WSCR linkage in comparison to the VDW tumor registry. Of the above, 21,600 (61%) tumor cases were reported within SEER region and 13,566 (39%) cases diagnosed in members residing outside of Puget Sound region. Of 21,600 SEER cases, 17,248 (88%) tumor cases were diagnosed after disenrollment from Group Health, and 2,090 SEER cases were found missing from VDW’s tumor registry. Due to lack of interpretable information, we were unsuccessful in linking 5,752 records in the VDW tumor registry with WSCR data. Discussion: In the absence of unique identifier, the project demonstrates a successful linkage using Link Plus. We now have cancer incidence and survival data for all Group Health members, not limited to Puget Sound region. This will augment cancer studies involving members from both SEER region and outside of Puget Sound counties

OpenNotes® –– Building Relationships With Patients for Better Health and Health Care

Background/Aims: OpenNotes® is a nationwide initiative supported by Robert Wood Johnson Foundation that offers patients ready access to progress notes of doctors and other clinicians after an office visit. In the original OpenNotes demonstration and evaluation project, patients were invited over email to read a note once it was signed by a primary care physician. The majority of patients in the study viewed notes and reported clinically relevant benefits. We know little about how many patients will view notes in the absence of a notification from a provider once the note is signed. The objective of this poster is to perform descriptive study on Epic/Clarity page views data and provide insights on how many notes are hidden and viewed using desktop/mobile devices. Methods: We tracked all the face-to-face visits to the providers and associated notes data in the electronic health record (EHR) from April 10, 2015, to Sept. 10, 2015. We then linked EHR data to web server activity log based on medical record number and date to quantify notes accessed via desktop/mobile devices and mobile applications. We used Venn diagrams and Microsoft Excel graphs for visualization of results. Results: Of the 679,111 outpatient visits during the period, 90% (610,625) had any associated note, 10% (68,486) did not, 96% (608,249) were accessible and 4% (2,376) were hidden from patient. Only 5.7% (34,780) of notes were viewed at least once during study period. Overall, there were 104,071 note views, i.e. 54,755 notes viewed at least once during the 5 months regardless of visit date, of which 19,975 notes were related to visits prior to April 10, 2015. We were able to match 104,019 (99.95%) EHR data to web activity log data. There were 52 (0.05%) unmatched records specific to Epic/Clarity alone. Web browsers accounted for 91.7% (95,473) of views [80.4% (83,662) desktop, 11.35% (11,811) mobile], 4.7% (1,470) were AVS views on Group Health mobile application and 6.8% (7,076) were viewed on unknown device. Conclusion: Desktop and mobile browsers continue to be popular platforms for viewing clinicians’ notes. One of the reasons for low progress notes views is attributed to lack of active promotion on availability of EHR notes by Group Health Cooperation at that time. Group Health clinicians will continue to inform their patients that they can review the after-visit notes online

Interactive Visualization of a Patient’s Electronic Health Information to Assist Manual Chart Review Using Tableau® Software

Background: Identification of many important health conditions requires synthesis of subtle clinical observations diffusely recorded in electronic health record (EHR) notes, making manual chart review the only viable method of collecting such information. Clinical documentation of problem use of prescription opioids, including abuse and addiction, is an example. A research project we conducted required determining whether problem opioid use was documented in the charts of 2,000 patients receiving chronic opioid therapy during a 9-year observation period. To enhance thoroughness and efficiency, we provided chart abstractors interactive graphical summaries of selected information from each patient’s EHR as an abstraction aid. Methods: We used pilot chart reviews and expert opinion to identify four types of information considered useful for identifying clinical documentation of problem opioid use: 1) the timing, type and days’ supply of opioid fills; 2) encounters; 3) diagnoses including behavioral health, substance abuse and chronic pain conditions; and 4) mentions in clinical notes of terms related to problem opioid use. We obtained structured data from the virtual data warehouse. We used natural language processing (NLP) to extract information from clinical notes. We created longitudinal graphical displays of each content area using Tableau® (www.Tableau.com), a business intelligence software product that simplifies the creation of graphical representations and real-time exploration of complex data. Graphics were juxtaposed on “dashboards” with shared time scales and drillable details (eg, details of medications fill, text surrounding NLP-extracted terms) facilitating a visual synthesis of multiple types of potentially relevant information. Results: Five experienced chart abstractors used the interactive graphics on one screen and the Epic® EHR interface on a second screen to conduct each chart review. Abstractors reported that the graphics facilitated efficiency through more rapid detection of periods of care in which problem opioid use may be documented (eg, emergency room encounters coinciding with early opioid refills). Particularly valuable was the ability to “see the larger picture” while also being able to drill into the details of specific events. Conclusion: Graphical visualization of information from EHRs can assist manual abstraction of health conditions when determinations about the presence of those conditions require synthesis of diffusely recorded content in voluminous charts

Patterns of antihypertensive and statin adherence prior to dementia: findings from the adult changes in thought study

Abstract Background Detecting patients with undiagnosed dementia is an important clinical challenge. Changes in medication adherence might represent an early sign of cognitive impairment. We sought to examine antihypertensive and statin adherence trajectories in community-dwelling older adults, comparing people who went on to develop dementia to those who did not. Methods We analyzed data from Adult Changes in Thought (ACT), a population-based cohort study embedded within an integrated healthcare delivery system. Analyses included 4368 participants aged ≥65 years who had at least one follow-up visit. Research-quality dementia diagnoses were used to identify cases. We selected non-dementia control visits matched on age, sex, and study cohort that occurred at similar ACT follow-up time as the case’s dementia onset; we treated this as the index date. Participants were included if they were prevalent users of either a statin or antihypertensive medication on the first day of follow up – 3 years prior to the index date. Using prescription fill dates and days supply, we calculated daily binary medication availability measures for each participant (‘days covered’) over 3 years leading up to the index date. We used group-based trajectory models to identify patterns of antihypertensive and statin adherence, and used conditional logistic regression to examine associations between adherence trajectories and dementia. Results Four trajectories were identified for antihypertensive users (292 cases, 3890 control visits), including near perfect (n = 1877, 36.6% cases, 45.5% controls), high (n = 1840, 43.2% cases, 44.1% controls), moderate (n = 365, 18.5% cases, 8.0% controls) and early poor adherence (n = 100, 1.7% cases, 2.4% controls). Odds of dementia was 3 times greater for those with moderate antihypertensive adherence compared to those with near perfect adherence (adjusted OR 3.0, 95% CI 2.0, 4.3). Four trajectories were identified for statin users (148 cases, 1131 control visits), including high (n = 1004, 75.0% cases, 79.0% controls), moderate (n = 192, 19.6% cases, 14.4% controls), early poor (n = 43, 2.0% cases, 3.5% controls), and delayed poor adherence (n = 40, 3.4% cases, 3.1% controls). No association was detected between statin adherence trajectories and dementia. Conclusions Patterns of medication adherence may be useful to identify a subset of people at higher likelihood of developing dementia

The Cancer Financial Experience (CAFÉ) study: randomized controlled trial of a financial navigation intervention to address cancer-related financial hardship.

BackgroundThere is an urgent need for evidence on how interventions can prevent or mitigate cancer-related financial hardship. Our objectives are to compare self-reported financial hardship, quality of life, and health services use between patients receiving a financial navigation intervention versus a comparison group at 12 months follow-up, and to assess patient-level factors associated with dose received of a financial navigation intervention.MethodsThe Cancer Financial Experience (CAFÉ) study is a multi-site randomized controlled trial (RCT) with individual-level randomization. Participants will be offered either brief (one financial navigation cycle, Arm 2) or extended (three financial navigation cycles, Arm 3) financial navigation. The intervention period for both Arms 2 and 3 is 6 months. The comparison group (Arm 1) will receive enhanced usual care. The setting for the CAFÉ study is the medical oncology and radiation oncology clinics at two integrated health systems in the Pacific Northwest. Inclusion criteria includes age 18 or older with a recent cancer diagnosis and visit to a study clinic as identified through administrative data. Outcomes will be assessed at 12-month follow-up. Primary outcomes are self-reported financial distress and health-related quality of life. Secondary outcomes are delayed or foregone care; receipt of medical financial assistance; and account delinquency. A mixed methods exploratory analysis will investigate factors associated with total intervention dose received.DiscussionThe CAFÉ study will provide much-needed early trial evidence on the impact of financial navigation in reducing cancer-related financial hardship. It is theory-informed, clinic-based, aligned with patient preferences, and has been developed following preliminary qualitative studies and stakeholder input. By design, it will provide prospective evidence on the potential benefits of financial navigation on patient-relevant cancer outcomes. The CAFÉ trial's strengths include its broad inclusion criteria, its equity-focused sampling plan, its novel intervention developed in partnership with clinical and operations stakeholders, and mixed methods secondary analyses related to intervention dose offered and dose received. The resulting analytic dataset will allow for rich mixed methods analysis and provide critical information related to implementation of the intervention should it prove effective.Trial registrationClinicalTrials.gov NCT05018000 . August 23, 2021