McKesson (United States)

Abstract This article describes a study of One Nation/One Project, a national post-pandemic arts-and-public-health initiative in the US. This values-based, convergent mixed-methods study found significant associations between arts participation, social cohesion, and well-being and offers a theory of change that illustrates these associations, as well as how arts participation increased social cohesion and enhanced well-being. At a moment when the severities of social divisions and loneliness are increasing in the US, this theory of change may enhance socia cohesion and well-being by encouraging cross-sector collaboration between the arts, public health, and municipal sectors and supporting investment in the arts.

599 Background: The White House Cancer Moonshot initiative aims to reduce cancer death rates by half before 2047 and emphasizes the importance of access to clinical data to improve patient outcomes through enhanced data standardization and interoperability. Effective data capture remains a critical challenge with manual abstraction consuming significant clinician administrative time. The US Oncology Network (The Network) partnered with Ontada to leverage artificial intelligence (AI) and Fast Healthcare Interoperability Resources (FHIR) standards to advance clinical data submission for the Center for Medicare and Medicaid Innovation (CMMI) Enhancing Oncology Model (EOM). Methods: A pilot study was conducted with The Network (Compass Oncology) to implement Ontada's AI-driven technology to submit 58 clinical data elements for 232 patients required for EOM Performance Period 1. Structured clinical data elements were sourced from iKnowMed Generation 2 EHR using FHIR mCODE (Minimal Common Oncology Data Elements) HL7 industry standards. Natural Language Processing (NLP) and AI models were developed to enhance data capture from unstructured clinical documentation for diagnosis date, primary tumor characteristics, regional lymph nodes, distant metastases, and histology. A time and motion study was conducted via manual chart abstraction to measure administrative time reduction and data quality improvements. Data was submitted to CMMI via FHIR. Results: Across five critical data elements representing 909 possible data points analyzed for this study, 53.5% were sourced from structured data vs. 40.6% from unstructured clinical documentation using AI. The time and motion study confirmed a 38% reduction in provider administrative time. The AI-driven submission successfully achieved 98% data completeness for EOM patients, exceeding the 90% threshold required by CMMI. Conclusions: Results from the pilot study underscore the transformative potential of AI and data interoperability in oncology care. By leveraging NLP technology and FHIR-compliant data standards, substantial improvements in data capture were achieved while significantly reducing administrative burden. This supports the Cancer Moonshot vision for standardized, interoperable oncology data and provides a scalable model for enhancing practice efficiency. The success of this pilot establishes a foundation for expanded implementation across additional practices to meet future EOM data submission requirements. Source of clinical data elements. Data Element Structured Unstructured-AI Manual Chart Abstraction Not Found Total Diagnosis Date 106 108 16 2 232 Primary Tumor (T) 99 47 3 0 149 Regional Nodes (N) 98 42 8 0 148 Distant Mets (M) 96 43 9 0 148 Histology 87 129 13 3 232 Total 486 (53.5%) 369 (40.6%) 49 (5.4%) 5 (0.5%) 909 (100%)

Low-birth weight infants are at risk for neurodevelopmental complications. Prolonged mechanical ventilation and endotracheal tube stability can compromise developmentally supportive positioning and result in positional head deformity (PHD). Nationally, PHD prevalence is 22%-66%, yet an internal audit of 93 neonatal intensive care unit (NICU) discharge summaries found normal head assessments. To evaluate PHD prevalence among NICU preterm infants and neurodevelopmental positioning practice. A convenience sample of 50 NICU preterm infants >72hours of life discharged alive were selected to establish PHD prevalence. Positioning was observed on all active NICU infants. Infants with other deformities or receiving palliative care were excluded. Measures included gender, gestational age (GA) and birth weight, length of stay (LOS), positioning using the Infant Positioning Assessment Tool (IPAT), and the investigator's head shape assessment at discharge. The prevalence of PHD was 12% (n=6) though clinician notes reported normal findings. Infants with PHD had a significantly lower GA at birth ( P =.010), and at discharge, had a smaller head ( P =.027) and a longer LOS ( P =.008). Positioning was observed on 78 infants over 4 consecutive weeks; mean GA=31.29±0.41weeks; weight=1713.56±83.70 g. Of the 572 observations, 84% were therapeutic; hand positioning had the lowest scores. The PHD prevalence rate for low birth-weight infants is likely underreported. The IPAT hand position element may need validation for extremely low birth-weight infants. Better documentation structures are needed to accurately describe and trend infant head shape.

Purpose: This article examines the transformative impact of data analytics on pharmaceutical distribution centre operations through centralization initiatives. The study investigates how analytics-driven centralization simultaneously addresses the seemingly competing objectives of cost reduction and service improvement in pharmaceutical distribution networks, exploring the multifaceted benefits across economic, operational, and strategic dimensions. Methodology: The research employs a mixed-methods approach combining quantitative operational data analysis from five pharmaceutical distribution networks representing 37 distribution centres over a 24-month period with qualitative insights from 28 industry practitioners. The study captures both pre- and post-centralization performance metrics, utilizing difference-in-differences analysis, time series modelling, and multivariate regression to isolate the causal impact of centralization initiatives. Findings: Centralized distribution operations yield substantial improvements including 14.7% reduction in operating costs, fill rate increases from 91.3% to 96.7%, 31% improvement in demand forecasting accuracy, and 73% reduction in compliance-related incidents. The research reveals that real-time visibility enables 94% faster decision-making, more efficient stock imbalance detection, and enhanced responsiveness to supply chain disruptions. Beyond operational benefits, centralization creates strategic advantages including improved customer satisfaction, competitive differentiation, and organizational agility. Unique Contribution to Theory, Policy and Practice: This study addresses a significant gap in the literature by integrating technological, operational, and organizational perspectives on pharmaceutical distribution centralization. It provides the first comprehensive framework demonstrating how analytics-driven centralization can simultaneously optimize cost efficiency and service levels in highly regulated environments. The research offers practitioners actionable implementation strategies and mitigation approaches for common challenges, while contributing to supply chain theory by establishing the synergistic relationship between network-level analytics and operational performance in pharmaceutical distribution contexts.

Cervical cancer is a vital cancer disease for women, which needs to be predicted using various technical tools. Although different computational learning models have been used to test the disease in many cases, their performance needs to improve according to appropriate methods. Thus, this paper proposes developing an aggregated deep-learning model to perform on the cervical cancer disease dataset. The proposed model creates predefined data from the original dataset and utilizes it for various methods, including convolutional, recurrent, artificial neural networks, and long short-term memory. Those methods are evaluated based on their approaches using the cervical cancer dataset. The proposed model is demonstrated on the cervical cancer disease dataset and analyzed in terms of individual and comparative performance. The performance of the proposed model is also improved by more than 95% on each evaluation parameter.

The Enhancing Oncology Model (EOM) is a voluntary, risk-based payment model implemented by the Centers for Medicare & Medicaid Services (CMS) to improve cancer care while reducing the total cost of care (TCOC). The US Oncology Network (The Network) comprises approximately 50% of all prescribers participating in EOM nationwide across 12 practice sites. In The Network, drug costs represented an average of 63% of a patient's TCOC. The aim of this study was to demonstrate the impact of a remote clinical pharmacist in reducing TCOC in the EOM. Medication initiatives were clinically evaluated and adopted at an individual practice level and included: monoclonal antibody (moAB) dose rounding, pembrolizumab dose banding, biosimilar therapeutic interchange (TIC), use of a preferred PD-1 agent, decreased up-front usage of long-acting growth factor in metastatic cancer, and use of zoledronic acid over alternatives. ClinReview pharmacists (CRPs) remotely reviewed oncology treatment orders for cost-saving opportunities and updated orders per protocols. Interventions were tracked by the CRP, and TCOC reduction was calculated using the difference between the CMS allowable for the original treatment ordered and the new order. From July 1, 2023, to December 31, 2024, seven CRPs within five of The Network's EOM participating practices evaluated over 5,600 patients. A total of 1,180 interventions were accepted, with moAB dose rounding and TIC being top contributors. The projected sum of TCOC reduction amounted to $8,982,235, or $1,604 USD per patient. In addition to the six initiatives, the CRP contributed an additional $1,201,326 USD in medication savings associated with drug selection. CRP's medication initiatives within The Network's EOM participation reduced TCOC by nearly $9 USD million, highlighting the potential for pharmacist-driven interventions to lower costs and drive the success of value-based care models in oncology practices.

e13565 Background: Community oncology practices bridge the gap between the growing disparity of increased cancer incidence in rural areas and a declining oncology workforce. A disintegrated system for cancer drug purchasing and reimbursement led to increased drug prices and reduced margins for oncology practices. Diverse insurer landscapes and step edits challenge community oncology practice independence to make drug selections to ensure financial viability. Pharmacists at The US Oncology Network (The Network) collaborated with individual practices to develop a strategy for using biosimilars and other clinically equivalent products to improve financial outcomes and ensure patient access to treatments. Methods: Pharmacists supporting The Network created and implemented a strategy and grid to execute therapeutic interchange (TIC). This provided guidance for selecting an optimal agent in a class for a payer using formulary, reimbursement, and purchasing data. Pharmacists used the grid to execute TIC approved by the practice. If the patient’s insurance was not in the grid, the pharmacist would choose the most preferred biosimilar and may receive feedback from the authorization team to switch the agent based on insurance preference in a process documented as “re-work”. The rework data was analyzed over a 6-month period to determine the effect payer-targeted TIC strategies had on the amount of clinical pharmacist rework. In addition, a single strategic initiative, focused on a particular payer and class, was determined by the individual practice, and subjected to monthly monitoring across 16 practices, each with a defined target objective to improve practice financial health. Results: From July 2024 through December 2024 clinical pharmacists used the grids to make 9,908 product substitutions across 16 oncology practices within The Network to a therapeutically equivalent alternative. All 16 practices created a payer-targeted drug initiative and goal, and fourteen (87.5%) practices achieved that goal in a 6-month period, suggesting positive economic gains for these individual practices. Prior to the implementation of grids, 7.9% of product substitutions required rework due to insurance formulary uncertainty. The rework was reduced to 3.5% after implementation of these grids, suggesting an absolute reduction of 4.5% and a relative reduction of 57%. The strategy reduced uncertain interchanges from 321 to 4, improving accuracy of initial TIC and creating a more streamlined workflow between the pharmacy and authorization teams. Conclusions: A payer-targeted TIC strategy and grid enabled community oncology sites to meet financial goals with decreased rework. This can be scaled to serve large patient populations with heterogeneous payer mixes. Pharmacists can play a key role in strategizing and executing these initiatives based on their knowledge of drug margins and therapeutic equivalency.

As emerging technologies such as Blockchain, the Internet of Things (IoT), and Artificial Intelligence (AI) continue to reshape industries, the need for robust data governance frameworks has become increasingly critical. These technologies introduce unique challenges, including data privacy concerns, security vulnerabilities, and the complexity of managing vast, decentralized data sets. This paper proposes a conceptual framework for data governance tailored to the specific requirements of Blockchain, IoT, and AI technologies. The framework emphasizes a holistic approach, integrating key governance principles such as transparency, accountability, and compliance with regulatory standards. It also highlights the importance of fostering collaboration between stakeholders, including technologists, legal experts, and policymakers, to create a cohesive governance structure that can adapt to the rapid evolution of these technologies. The proposed framework addresses three core areas: data integrity and quality, security and privacy, and ethical considerations. For Blockchain, the focus is on ensuring the immutability and transparency of records while safeguarding against potential misuse of decentralized data. In the context of IoT, the framework prioritizes the management of data from diverse sources, ensuring interoperability and protecting sensitive information from unauthorized access. For AI, the emphasis is on developing ethical guidelines for data usage, preventing bias in algorithmic decision-making, and maintaining transparency in AI-driven processes. The framework also advocates for the integration of advanced data analytics and machine learning techniques to enhance data governance capabilities, enabling real-time monitoring and predictive insights. Additionally, it underscores the need for continuous training and education for all stakeholders to keep pace with the dynamic nature of emerging technologies. By adopting this comprehensive data governance framework, organizations can mitigate risks, ensure compliance, and harness the full potential of Blockchain, IoT, and AI while maintaining public trust.