bluuebunny/crossref_metadata_2025_split huggingface dataset:
Improving implementation of standardized structured reporting: Development of an implementation toolkit.
Authors: Hermens Rosella , Swillens Julie Elisa Maria , Voorham Rinus , Nagtegaal Iris D. 
Published: October 2019

265 Background: Although standardized structured reporting (SSR) usage is recommended in national and international evidence-based guidelines (warranting quality & safety), actual use varies widely in oncology related medical disciplines. Previous research showed multiple impeding factors regarding SSR-implementation. The aim of this study is to develop an implementation toolkit, based on the impeding factors found. Methods: We organized an expert meeting a. to explore experts’ opinions on the impeding factors found in a previous study; and b. to discuss possibilities for a tailored implementation toolkit. The expert-panel consisted of three pathologists, three multidisciplinary-team (MDT) members, three patient organization delegates, two delegates of the Dutch nationwide network and registry of histo- and cytopathology(PALGA), two delegates of the Netherlands Comprehensive Cancer Organization (IKNL), one PALGA-liaison, and one implementation expert. A literature study was performed to obtain evidence on the most effective implementation tools. All selected tools were included in an implementation toolkit. Results: The expert meeting and literature study yielded eight tools to be developed: 1. Two communication tools of both the benefits of SSR and of the development and updates of SSR-templates; 2. Consultation and simulation sessions with MDT-members to optimize the development and feedback process of the standardized structured reports and to increase familiarity with these reports; 3. A tailored e-learning for pathologists to increase the familiarity, knowledge and skills of SSR-usage; 4. IT-solutions, such as incorporation of speech recognition to improve the usability and content of both the SSR templates and the generated reports; 5. A sustainable feedback-system to support SSR-template usage; 6. Agenda setting success to encourage use of SSR. Conclusions: Experts proposed several tools to improve the implementation of SSR. The next step is to test this toolkit in a pilot study.
Can utilization of clinical pathways (CPs) effectively reduce drug spending (DS) within the Oncology Care Model (OCM)?
Authors: Hertler Andrew Allan , Khetarpal Rani , Wade James Lloyd , Bassin Ed , Chau Sang , Damarla Vijay Kumar 
Published: October 2019

269 Background: It is well known that one of the biggest challenges in the OCM is being able to reduce DS. DS accounts for approximately 50% of episode costs within OCM. New Century Health (NCH) is well known for its value-based care CPs, with a history of successfully controlling DS and taking risk on behalf of commercial payors in oncology. Cancer Care Specialists of Illinois (CCSI), is a community-based oncology private practice spread throughout rural Illinois and is an OCM participant with 14 Medical Oncologists and 4 Mid-Level providers, 6 Radiation Oncologists, and 1 Urologic Oncologist. CCSI engaged NCH in a risk- based relationship to provide support and resources that will allow maximization of OCM episodic cost savings. This included utilization of NCH’s CPs. Methods: Starting in October 2017, CCSI utilized NCHs pathways for OCM attributed patients. NCH CPs were embedded in all CCSI physician computers for rapid access and reference decision making. All treatment plans were submitted in real time to NCH for CP adherence measurement. Analysis was conducted prior to implementation and on an ongoing daily & weekly basis to identify higher cost drugs or regimens. A joint CCSI/ NCH clinical data governance committee met bi-weekly to review CP performance metrics and drug utilization. The goal was to encourage treatment choices favoring both efficacy and value. Results: Post analysis revealed that over a 15-month period (Oct 2017 to Jan 2019), a 13.5% reduction in overall DS, which is equivalent to ~$250k per physician was achieved. This contributed to an overall reduction in total cost of care by 5% as compared to the OCM median. Post analysis also revealed an increase in pathway adherence from a baseline of 69% to 81% at the end of the measurement period. Conclusions: Reduction in DS is possible within a VBC model, through the use of value-based CPs. Important factors contributing to the reduction in DS include physician adherence rates, real time identification of high cost drugs/regimens, availability of immediate peer to peer discussion, rapid desktop access to a catalog of higher value alternate therapies, and continuous collaboration between CCSI and NCH through the governance committee.
Understanding the challenges for oncologists in predicting the end-of-life phase of care in cancer patients with advanced solid tumor diagnoses.
Authors: Lyss Aaron J. , Crouse Cheryl A. , Dickson Natalie R. , Patton Jeffrey , Waynick Christopher A. , Schleicher Stephen Matthew 
Published: October 2019

281 Background: Early advanced care planning and palliative care improves outcomes during the end-of-life phase of care (EOL) for metastatic cancer patients. Identifying patients who are likely to transition to EOL is a necessary step to prioritize limited palliative care resources and is integral to success in value-based payment models. We analyzed whether physician documentation of prognosis in a clinical pathways system (CPS) could reliably predict when patients are nearing EOL for a large community oncology practice of more than 70 medical oncologists. Methods: Tennessee Oncology (TO) requires physicians to use CPS for all Medicare patients. CPS prompts physicians to answer the “prognostic question” “would you be surprised if this patient died in the next year?” for all OCM patients with advanced solid tumors at the beginning of treatment or at the time of a change in treatment plan. Prognostic question responses were compared to actual dates of death documented in the practice management system. Results: A total of 5,266 distinct patients were expected to trigger an OCM episode during 2017. The CPS prompted a response to the prognostic question for 1,228 (23%) of these OCM patients. There were 665 (54%) positive prognoses (expect patient to live more than 1 year) and 563 (46%) negative prognoses (expect patient to die within 1 year). Physicians documented accurate prognoses in 712 (58%) of cases. For patients with positive prognosis 557 (84%) were accurate. For patients with negative prognosis 155 (21.8%) were accurate. Conclusions: We found that for patients with terminal cancer, it is difficult for physicians to accurately predict prognosis. These findings support the importance of ASCO guidelines pertaining to patient access to palliative care during the entirety of cancer treatment for all patients with metastatic cancer. [Table: see text]
Primary non-adherence to adjuvant endocrine therapy in older women with breast cancer.
Authors: Tchuente Vanina , Stern Donna , Prchal Jaroslav , Martin Judy , Tamblyn Robyn , Meguerditchian Ari N. 
Published: October 2019

285 Background: Adjuvant endocrine therapy (AET) improves survival in hormone receptor positive breast cancer (HR+BC). Challenges with adherence to AET in seniors are well documented; however, there is limited knowledge on primary non-adherence (PNAD). PNAD is defined as non-initiation of a prescribed medication. Our aim is to characterize PNAD rates in women aged ≥ 65 with HR+BC and identify potential predictors, using real-time treatment information. Methods: Optimum is an e-health platform integrating real-time analysis of administrative claims data combined to patient-level clinical information on breast cancer. Optimum tracks care trajectories to identify deviations from best practice, using data from Quebec’s universal health insurance plan that covers all medical and pharmaceutical care. In this single-center feasibility study, we characterized PNAD as a non-initiation of AET within 10 days from the first prescription. Descriptive analyses were used to assess potential predictors. Results: Of the 57 patients enrolled, 9 were excluded due to lack of > 30 day follow up. In the remaining 48 patients, PNAD was 21 %. Baseline Charlson comorbidity index (0 vs 13 %), psychotropic drug use (20 % vs 26 %) and polypharmacy rate (10 % vs 11 %) were lower in PNAD patients, compared to primary-adherent patients. PNAD patients had larger average tumor size (1.8 cm vs 1.6 cm), more often overexpressing HER2NEU (10 % vs 3 %), more negative progesterone receptor (10 % vs 5 %). They also more often had lumpectomy (70 % vs 65 %), SLNB (70 % vs 58 %) and more frequent margin revisions (30 % vs 16 %). They more often received chemotherapy (30 % vs 0 %). At 30-day follow-up, 40 % of PNAD patients had not yet initiated AET. Conclusions: This study confirms the feasibility of combining real-time administrative data and patient-level clinical information to assess breast cancer quality care. PNAD in women with HR+BC was higher than expected. PNAD patients had less comorbidities and drug use, but more aggressive cancers and more often also had quality challenges with surgical care (margin revision). PNAD predictors can potentially be used to identify patients that may require additional support to optimize disease management.
Daily step counts to predict hospitalizations during concurrent chemoradiotherapy for solid tumors.
Authors: Izmailova Elena , Huang Chengrui , Cantor Matthew , Ellis Robert , Ohri Nitin 
Published: October 2019

293 Background: Wearable activity trackers could be useful tools in cancer care. Potential gains include better selection of patients fit for aggressive treatment, improved evaluation of patients during treatment, and provision of enhanced supportive care for patients. In a pilot study, low daily step counts were associated with increased risk for hospital admission during concurrent chemoradiotherapy for solid tumors of the head/neck, lung, or gastrointestinal tract. Here we seek to refine those findings using a larger patient cohort. Methods: Study subjects wore a commercial fitness tracker during a course of concurrent chemoradiotherapy. Patient characteristics, daily step counts, and hospital admissions between the initiation and completion of radiotherapy were tabulated. EORTC QLQ-C30 questionnaires were completed weekly, and summary QoL (range: 0-100) for each subject and week were calculated. Cox regression models with time-dependent and time-fixed covariates were used to identify predictors of first hospital admission during the radiotherapy course. Results: 188 subjects from three trials met inclusion criteria. The most common cancer diagnoses were head and neck (31%), lung (28%), and a gastrointestinal (30%, most commonly rectal [10%] or esophageal [6%]). Median age was 61 (range: 27-90). Baseline ECOG performance status was 0 for 46% of subjects, 1 for 47%, and 2 for 7%. The median patient compliance of wearing activity trackers and contributing data was 98%; 38subjects (20%) were hospitalized during the radiotherapy course. Cox regression demonstrated a 36% reduction in the risk of hospitalization for every 1000 daily steps (averaged over the past 3 days; hazard ratio 0.62, 95% confidence interval 0.48-0.81, p=0.0005). Diagnosis, age, baseline performance status, and QoL score were not statistically significant predictors of hospitalization risk. Conclusions: Continuous activity monitoring with wearable devices during concurrent chemoradiotherapy is feasible. We have validated daily step counts as dynamic predictors of hospitalization risk during treatment. Interventional trials should leverage activity data to optimize supportive care during cancer therapy.
A physician engagement learning strategy to reduce care variation in breast cancer across a clinically integrated network.
Authors: Burgon Trever Bradley , Richards Jon M. , Nelson Carrie E. , Paculdo David R. , Tamondong-Lachica Diana , Liangco Wilfredo L. , Peabody John Williamson 
Published: October 2019

30 Background: Advocate Physician Partners (APP) is a 5,000+ physician CIN associated with Advocate Aurora Health. APP wanted to standardize care among their 100+ medical oncologists who work in 20+ independent and employed practices. We approached standardization as a learning opportunity that could be best done through active engagement. Methods: From January 2016 to November 2017, we utilized the Clinical Performance and Value (CPVÒ) simulated patient platform to measure clinical practice and provide feedback across APP. Over six rounds, a total of 119 APP medical oncologists were scored against evidence-based work-up and treatment criteria. To determine if any of the learnings found on the CPVs translated into practice, we used registry and claims data on unnecessary imaging for early stage breast cancer. Results: In the simulated breast cancer patients, we found an overall improvement of 13.3% in primary diagnosis accuracy and 15.3% in treatment (p<0.001 for both). Preferred guideline-based hormonal therapy and chemotherapy similarly improved by 15.1% (p=0.018). To compare the CPV improvements with patient-level improvements, we looked specifically at unnecessary imaging and testing which declined overall by 35.7% (p=0.006); the associated costs fell by 31.3%. In early stage breast cancer, the CPV-measured use rates for CT or PET/CT (p=0.026) and other scans (p=0.012) decreased, showing better alignment with guidelines (see Table). When we analyzed the patient claims data, we found these showed similar declines in potentially unneeded breast cancer testing. Conclusions: Active learning, using CPV simulations, increased adherence to evidence-based practices, which translated into practice as evidenced by registry/claims data showing fewer unnecessary imaging in early stage breast cancer. [Table: see text]
Structuring clinical pathways: Creating common language where there is none.
Authors: Hamilton Joanna M. , McNeil Debra , Milne Dana , Hayne Victoria , Holtz Laura , Jackman David Michael , Jacobson Joseph O. , Leblebjian Houry , Meserve Emily , Methot John , Wolfson Matthew , Tremonti Carole Kathleen 
Published: October 2019

303 Background: Clinical pathways (PW) consist of Decision Criteria (DC) such as patient and disease characteristics, prior therapy, and genetic tests. Historical Dana-Farber Pathways (DFP) content was unstructured and maintained in static documents. This could lead to inconsistencies across and within PW, limits to the scope of DFP analytics, and potential discrepancies in clinical content. Methods: To transform DFP content into a digitally innovative structure the DFP team created a hierarchical Data Model (DM). The team compiled all unstructured DC in historical PW, organized them into parameters and attributes, and connected them to external ontologies (e.g., ICDO3) where appropriate. The team then applied the structured DC to historical PW to test comprehensiveness; and addressed any gaps identified in PW and the DM. Results: The DPF DM contains 32 parameters (e.g., Diagnosis) and 218 attributes (e.g., Group Stage) that can be combined to represent all 600+ pathway branch points. The comprehensiveness and nuance of the DM improves DFP’s specificity and clinical flexibility: The DM ensures that DFP gathers actionable data across all PW and creates common language that allows for disease-specific nuances; The DM rectifies gaps in historical PW, such as DC that were not mutually exclusive or conflated multiple clinical parameters; The DM creates complex DC to direct specific sub-groups of patients to the correct treatment path, even in cases where treatment recommendations differ within diagnostic groups. Conclusions: Structuring and standardizing PW content is a complex, time-intensive endeavor. However, this work addresses the challenges of managing clinical content and provides significant benefits for future PW development. A structured DM ensures PW are comprehensive, logical, and built on a framework of inter-operability standardization. A DM also allows DFP to connect with the EMR for auto-navigation, which streamlines provider workflow. The ongoing work required to build and maintain a structured PW DM is worth the result: rich actionable data that can illuminate and standardize practice patterns within and across diseases and institutions. It creates an insightful solution to broadly manage the cancer population.
Everyone needs robust analytics: Integrating medical ontologies into the Dana-Farber Pathways.
Authors: Milne Dana , Hamilton Joanna M. , McNeil Debra , Methot John , Jacobson Joseph O. , Tremonti Carole Kathleen , Jackman David Michael 
Published: October 2019

304 Background: The majority of clinical data is unstructured. Medical ontologies standardize the language used to communicate diagnoses, symptoms, and procedures used in healthcare. Incorporating medical ontologies into the Dana-Farber Pathways (DFP) provides structure and removes ambiguity by creating definitions for each data point. This exercise was critical for the development of the DFP into a fully automated platform that optimizes clinician workflow, reduces clicks, and expands the potential for data analysis. Methods: The DFP data model was reviewed to identify value sets contained in an existing medical ontology. DFP required employing multiple ontologies, which was difficult, as many ontologies overlap (e.g., SNOMED, NCI, ICD-O-3, ICD-10) and none were developed for this intention. For example, the majority of DFP necessitate site/histology as a criterion for decision-making. In our Electronic Data Warehouse (EDW), site/histology are stored as free text, as ICD-O-3 codes from the cancer registry, and as ICD-10 codes from Epic. To obtain site/histology in a structured format, both ICD-O-3 and ICD-10 were needed. Ongoing maintenance of the linkages between ontologies and value sets is required as ontologies are updated and DFP are enhanced. Results: The relationship between the DFP value set and the SNOMED ontology was 1:1. However, other relationships were not equal or straightforward. Some ontology values were too detailed for DFP. These were either grouped together (e.g. tumor site) or just one value was chosen (e.g. drug class). Others didn’t contain all the values required by DFP (e.g. regimens). Conclusions: DFP requires structured data related to ontologies but not identical to and not limited by the scope of ontologies. Choosing medical ontologies utilized in an EDW facilitates the implementation of auto-navigation, which ultimately streamlines provider workflow. A structured data model connected to ontologies collects valid and clearly defined data to support robust analytics. [Table: see text]
Untangling a web of Electronic Patient Reported Outcomes data to support quality improvement.
Authors: Davis Meggan , Winters Erik , Acomb Lisa , Palmgren Matt , McMillen Kerry K , Wong Tracy 
Published: October 2019

311 Background: Adoption of Electronic Patient Reported Outcomes (ePROs) as part of clinical care is aided by the ability to respond to feedback and develop solutions founded on data. This requires access to usage, volume, response, and system performance data, yet with limited resources the infrastructure needed to store and analyze these data is often an afterthought. The Seattle Caner Care Alliance followed an iterative process to create dashboards featuring content-specific metrics while improving the underlying infrastructure of patient-generated data collected via the ePRO program. Methods: For each dashboard, the project team followed 7 steps: 1) Clarify the purpose. 2) Brainstorm requirements with content-specific stakeholders. 3) Create initial drafts for review, testing, and validation. 4) Validate that metrics are clear and provide value. 5) Update dashboards and review with stakeholders. 6) Perform technical clean-up and improvement to data infrastructure. 7) Iterate until all stakeholders approve. Results: 27 data tables were linked to create four content-specific dashboards: Overall Key Performance Indicators (KPI), an experimental dashboard, and responses to chemotherapy symptom and distress screening surveys. The KPI dashboard featured completion rate, adoption rate, survey and intervention volumes with the ability to filter by ePRO type, diagnosis, and intervention. The experimental dashboard allowed the project team to test data points, visualizations and methodologies before creating dashboards or updating data structures. Chemotherapy symptom and distress screening dashboards displayed interventions by symptom or concern, severity, referrals, and response distribution. Conclusions: Data collected via an ePRO program offers an opportunity for improvement during implementation and ongoing program development. An iterative approach to analyzing these data and creating dashboards that engaged stakeholders and focused on data quality was an effective way to untangle a complex web of patient-generated data and to support program improvement. Future ePRO implementation would benefit from the inclusion of a flexible data development approach as part of planning and design.
A structured algorithm for judicious use of antibody test in diagnosis of heparin-induced thrombocytopenia (HIT).
Authors: Swarup Sriman , Ball Somedeb , Sultan Anita , Adhikari Nimesh , Thein Kyaw Zin , Swarup Khatrina , Awasthi Sanjay , Tijani Lukman Aderoju 
Published: October 2019

314 Background: HIT diagnosis combines 4T scoring & HIT antibodies (HIT-Ab) testing, with confirmatory serotonin release assay (SRA). Latex immunoturbidometric assay (LIA) shows promise in quick & accurate HIT diagnosis, compared to particle immunofiltration assay (PIFA). We designed a HIT workflow & replaced PIFA with LIA at our hospital, to improve 4T usage rates & HIT diagnosis accuracy in a cost-efficient manner. Methods: In phase I, patients (pts) charts with HIT-Ab (PIFA) ordered between Mar2017-Mar2018 were retrospectively reviewed. Three investigators independently calculated 4T & reviewed PIFA/SRA results, with any alternative anticoagulation (AC) used. In phase II, a new workflow on electronic health record incorporating 4T algorithm with HIT-Ab test order was implemented. Our lab replaced PIFA with LIA. In phase II, charts for pts with LIA results from Jan-Mar 2019 were reviewed. Results: In phase I, 170 pts had PIFA results & 5 such pts (0.02%) had 4T score documented in chart. Per investigators, 113 (66.4%) pts had 4T scores. PIFA showed 60% sensitivity & 50% specificity compared to SRA. 19 pts received AC (4 pts had low 4T with negative PIFA). In phase II, 40 pts with LIA tested, 4T scoring by ordering physicians improved to 100%. Per investigators, 42.5% pts had low 4T scores. Clinicians documented high 4T in 11 pts; investigators confirmed high 4T in 2 pts. AC was used in 5 pts (4 with intermediate-high 4T per investigators) Conclusions: Our study showed that incorporation of 4T scoring in HIT algorithm significantly reduced unnecessary ordering of HIT-Ab, with 100% compliance in 4T reporting. Use of AC was consistent with the level of probability by 4T. Further clinician education can help in reduction of up scoring of 4T noted in our results. [Table: see text]