Introduction
Data in healthcare are different: one group includes data characterizing clinical cases (DRGs, CPTs, ICDs); another includes population characteristics important for treatment (race, sex, age). Exploring the importance of these data will facilitate care processes and inform strategies to improve the quality of work. At the healthcare level, data enable international dialogue and support organizations, and in the hospital setting, it improves specific business processes.
Meaningful and Inefficient Data Use in Healthcare
DRGs
In healthcare facilities, DRGs improve access to care for all populations. On the other hand, in the hospital, DRGs regulate costs based on the aggregate of clinical cases (Cook & Averett, 2020). The less significant value of DRGs may be associated with improved health care quality.
CPTs
The significance of CPTs in healthcare lies in establishing a standard coding language across the industry. For the hospital, CPTs are tools for high-quality reporting to protect employees. A lesser use of CPTs is to unify care processes.
ICD
The ICD enables the coding of disease information and the creation of an international health database. At the hospital level, ICD-9 is a tool for statistics and clinical targets. Less critical is to facilitate the interpretation of results and simplify the discussion at the healthcare level.
Age
At the healthcare level, age serves as a differential characteristic that enables the development of treatment and diagnostic protocols tailored to each age group. At the hospital level, the age criterion allows the division of worker responsibilities to improve the quality of care. Less significance can be attributed to analyzing the relationship between age and clinical cases.
Gender
Gender is a dominant criterion in healthcare because it enables statistical analysis, highlights protocol patterns, and influences policy and culture. At the hospital level, gender as data is linked to gender-specific ward resource planning. Of lesser importance is the allocation of clinical workload to staff.
Race
Race as a criterion at the healthcare level reveals patterns in the development of certain diseases due to social discrimination. At the hospital level, race is a characteristic of a culturally diverse environment. Race’s relationship to disease is of lesser importance in the context of data.
Conclusion
Different data types allow healthcare organizations to manage their clinical goals and objectives. At the healthcare level, the primary purpose of data is to create a unified approach to treatment based on population criteria. At the hospital level, data enable the allocation of staff workload and the management of clinic resources. The practical application of data is less critical – particularly in terms of quality of care, interactions within healthcare, and clinical case analysis.
Reference
Cook, A., & Averett, S. (2020). Do hospitals respond to changing incentive structures? Evidence from Medicare’s 2007 DRG restructuring. Journal of Health Economics, 73, 102319.