Beyond the Clipboard — How Data Systems Are Reshaping Medical School Evaluation

From classroom observations to critical accreditation data, the way medical schools evaluate themselves is evolving. Much of that shift is being driven not by curriculum changes or new regulations alone, but also by how data is being managed, integrated, and surfaced for institutional decision-makers. Suhas Hanumanthaiah, who worked as a Data Architect at the Dean's Office of UCLA's David Geffen School of Medicine (DGSOM), offers a close-up view of this gradual change.

Hanumanthaiah's contributions at UCLA highlight how modern data systems are influencing academic evaluation in medicine. Tasked with designing the Financial Data Mart (a mini data warehouse focused only on financial information) for the Dean's Office, he worked directly with leadership, including the CFO, to architect a reporting infrastructure used across all departments of DGSOM. By developing rule-based data transformations based on the Finance Controller team's needs, he helped turn disparate transactional data into insights for strategic planning. The initiative earned him a Staff Appreciation and Recognition (STAR) Award.

He also supported inter-university research projects by acting as a liaison between the UCLA Infrastructure team and external collaborators. One of Hanumanthaiah's more public-facing projects involved supporting UCLA's annual reporting to the Board on employee race, gender, and ethnicity-a critical component of the university's inclusion efforts. Here, he helped Dean's office centralize data and build a secure, row-level security data model that protected individual privacy while still allowing analytics at scale. The solution proved flexible enough to be adopted outside the medical school, used by various departments across UCLA (not just DGSOM).

However, the scope of his work extended well beyond finance and HR. He played a key role in integrating evaluation and attendance data from external vendors into the university's data warehouse, for building required KPI (Key Performance Indicators) and research case studies of curriculum efficiency.

Further, as part of UCLA's digital transformation, Hanumanthaiah worked closely with contractors to transition old applications to modern platforms. In doing so, he often designed the backend for low-code/no-code tools that allowed business users, including researchers applying for grants, to operate with more agility.

The actions taken translated directly into measurable efficiency gains. For example, automating data integration through scripting reduced development time by 65%, and optimization of the reporting infrastructure improved report rendering speeds by 500%. He also led the development of data quality and master data management practices that improved the accuracy of complex financial calculations and transaction analyses.

While working on the projects to get the desired results, he tells us about some of the challenges he faced. One significant hurdle involved reconciling UCLA's broader campus accounting structure with DGSOM's internal standards. To address this, Hanumanthaiah implemented a control table-based logic system that simplified review and maintenance. Another complex task was migrating on-premise Power BI reports to the cloud while preserving Row Level Security-a feature critical for protecting sensitive HR data. He worked to build a new data model as a proof of concept and illustrated to the team how to migrate reports with Row Level Security.

He also supported inter-university research projects by acting as a liaison between the UCLA Infrastructure team and external collaborators, and contributed academically to the field. His paper, "Finance Projection Using Data Mining Algorithms on SSAS and Python Data Science Libraries," explores predictive modeling for finance, underscoring his interest in marrying traditional reporting with newer analytical tools.

From his perspective, financial data, depending on the source application, has evolved to keep up with regulatory standards. He advises, "Developing a data mart with clean and approved data is critical when transforming financial data to match industry standards. These transformations are usually complex and require regular maintenance to modify rules to manage outliers." The future, he believes, lies in cloud-based lakehouses, which allow for scalable, high-performance financial analytics. These platforms offer the velocity and computational strength needed to support complex, evolving data requirements in academic institutions.

Suhas Hanumanthaiah's journey reflects a broader shift in medical education-one where the clipboard is giving way to centralized data warehouses, row-level security, and predictive analytics. In this changing environment, data architects like Hanumanthaiah are integral to how modern medical schools measure their impact, allocate resources, and plan for an environment that enables growth.