Optimizing temporal aspects of your data.

Authors: Ramesh (Jwala) Vedantam , Karteek Y.

There’s a saying that the only constant is change. This applies to data too. In the intricate handling of data and analysis it enables, one principle remains universally true: data is ever-changing. Its temporal nature—how it evolves vs. remains constant over time—affects every industry, posing both challenges and opportunities for innovation. Understanding and managing these changes isn’t just about avoiding inaccuracies, such as false positives; It’s about embracing a strategic approach that enhances data quality, deepens insights, and maximizes the impact of data-driven initiatives.

While this principle applies across various sectors, healthcare provides a compelling illustration of how temporal data can be leveraged for significant advancements. However, the concepts and strategies discussed are equally relevant to other industries, from finance and manufacturing to education and technology.

Five broad categories of data, each with its own temporal characteristics have different impact and need? different management techniques as outlined below.

• Master Data: This foundational category includes patient data, clinician data, and facility information. It represents the static core around which healthcare services revolve, offering a consistent reference point for personalizing patient care, optimizing clinical workflows, and ensuring accurate communication across departments. The temporal aspect of master data lies in its need for regular updates to reflect changes such as new patient information, staff turnovers, or facility expansions, ensuring the data remains a reliable source of truth.
• Reference Data: Encompassing various codes, classifications, and standards used in healthcare, such as ICD codes for diagnoses and CPT codes for procedures, reference data is crucial for ensuring consistency and accuracy in healthcare documentation, billing, and analysis. While inherently more static than other data types, its temporal significance is observed in updates and revisions to coding standards, requiring healthcare organizations to adapt their systems and practices accordingly.
• Configuration Data: Encompasses the settings and parameters that dictate the operation of healthcare IT systems, medical devices, and software applications. This includes user preferences, system configurations, and rules for data processing. Configuration data plays a critical role in ensuring that healthcare technologies are tailored to the specific needs and workflows of healthcare organizations, offering operational flexibility and customization.
• Transaction Data: This category captures the dynamic interactions within healthcare, from clinical transactions like lab orders and medication prescriptions to financial transactions including billing and insurance claims. The temporal nature of transaction data is integral to operational and clinical decision-making, providing real-time insights into patient care processes, resource allocation, and financial management. Analyzing trends over time in this data can uncover opportunities for efficiency improvements, cost reduction, and enhanced patient experiences.
• External Data: External data such as research findings, industry trends, regulatory updates, and patient-generated health data from wearables and other devices, is a vast and varied domain. Its temporal relevance is particularly pronounced, as integrating and analyzing this data can offer healthcare organizations insights into emerging health threats, innovative treatments, and shifts in patient expectations. Staying abreast of changes with this data is essential for maintaining competitive edge and aligning healthcare services with best practices and patient needs.

Another dimension to managing temporal nature of data is to understand the nature of these changes and who/what is responsible for these changes. Keeping track of these broad types of changes can also be helpful in effective data management.

• Demographic changes: Demographic changes persons (customers, members, partners) such as age, gender, address, nationality, legal etc.,. constantly change with time. Being aware of these and keeping track of these changes are important in overall data management and is an important aspect to consider in MDM (Master Data Management) Programs.
• Geographic and/or Geopolitical changes: Political and natural causes create geopolitical changes in data over time. New counties are formed, new zip codes are created, New area codes in phone numbers are assigned all the time. Any of your data analytics programs referring to this data should be cognizant of these time sensitive changes
• Policy/Standards changes: These can be introduced by legal/governmental entities such as HIPAA, GDPR or changes introduced by standards committees/bodies such as ICD codes, X12 standards etc.,.

The Strategic Imperative of Managing Temporal Data

Effectively managing these diverse data types, each with its unique temporal dynamics, is a strategic imperative for organizations. It requires a nuanced understanding of how data evolves over time and the systems in place to capture, update, and analyze this data. Configuration data, in particular, underscores the need for adaptable and user-centric systems that can evolve with the changing demands of the industry.

Keeping time sensitive nature of data and creating strategic, architectural and programming provisions to manage is an integral and critical part of effective data management.

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