Benchmark and Quality Measures

Benchmark and Quality Measures

The highly sophisticated and diversified health system in contexts of healthcare delivery and health service levels calls for data compatibility which according to Shary, (2020), is an IT innovation that provides integrated data throughout an organization, among organizations, and across industries. Compatible data systems constitute a data network that works in harmony through interoperability.

Benchmark and Quality Measures

Data Compatibility

The particulars extracted from external databases comprising national and state healthcare information ought to be in line and compatible with office data for robust comparisons and conclusions to be made. Data obtained from numerous sources aids in juxtaposing and assessing statistical trends of healthcare issues such as cancer within a nation, hence gathering a vivid and full comprehension of the status of the condition. Subsequently, this process outlines trends and patterns over time that are useful in planning and healthcare resource allocation (Rafi & Greenland, 2020).

Similarly, compiling healthcare data facilitates improvement in patient care quality as well as a reduction in healthcare costs, since information on the recommended tests and treatment options of the health condition is readily available to healthcare providers at their disposal.

Data compatibility for my study will be enhanced by embracing the healthcare system’s standards for promoting healthcare information exchange (HIE). Arguably, this compatibility will simplify the process of data exchange and ensure data interoperability.  Additionally, I will track my data by selecting a master dataset and then critically checking each observation in the secondary database to ensure they are compatible.

To determine the compatibility of national or state data with my office data, I will standardize the data. Likewise, a careful assessment of the trends revealed by statistical analysis of the two sets of data will be done to determine the points of agreement and disagreement (Alteri, 2020).

Standardization of healthcare data creates consistency and makes it easy for end-users to utilize it by intensifying their understanding. Standardized data also decreases the burden associated with the collection of data from multiple sources. All aspects of my data including the condition, the methods, and the analysis will be standardized.

Nevertheless, the process of data standardization comes with its challenges. The diversified nature of the healthcare industry dictates the difficult task correlated with the standardization of healthcare data. For instance, the lack of efficient coordination of stakeholders and healthcare providers across the health system makes it difficult to reach a consensus to standardize healthcare data.

Moreover, the lack of a common method of patient identification across the entire healthcare network is evidenced by unique patient recognition techniques exercised by different healthcare facilities. This largely impedes all efforts to standardize healthcare information. In addition, ethical issues and legal considerations such as privacy and confidentiality when it comes to sensitivity of patient information contribute to hitches in the standardization of healthcare information.

Effects of Health Information Quality on HIE

Health information exchange is a pivotal objective of health technology investment due to its overwhelming benefits including but not limited to patient satisfaction, reduced healthcare costs, and improved patient care. Health information exchange is a network that permits accessibility and sharing of electronic patient-related health information records among healthcare providers and their patients (Payne et al., 2019).

Payne et al. (2019) further describe three types of HIE as direct exchange, query-based exchange, and consumer-mediated exchange. On the other hand, a national database in healthcare refers to a system for routinely recording patient clinical data and laboratory data by healthcare providers. These national databases act as a source of real-world evidence and facilitate medical research.

Submitting incomplete or rather inaccurate information to health information exchange system concerning patients’ clinical information record have devastating effects to the facility involved. These consequences range from inaccurate statistical databases and poor patient care to legal issues and settlement fees.

The image portrayed by the facility will also be impaired. At the same time, healthcare providers and researchers conducting patient-related studies will encounter stumbling blocks that could compromise patient care safety due to alterations in evidence-based information for clinical practice.

Meanwhile, submitting incomplete and inaccurate information to a national database would significantly reduce gross revenues and reimbursements of the implicated facility. This would cause a decrease in patient care safety and quality along with an inability of the staff to optimally provided the desired patient care due to lack of funding.

In a case where my proposal encounters incomplete or inaccurate data, completion of the study will not be feasible due to inaccuracies that lead to discrepancies. Similarly, insufficient data connotes ineffective compilation of statistical trends. Therefore, uploading patient healthcare information in HIE or national database mandates healthcare providers to be observant.


Data compatibility is elemental while conducting explorations related to healthcare data. The importance of data compatibility in ensuring accurate information cannot be overstated. Healthcare providers should, by all means, ensure that accurate patient-related information is uploaded in the HIE and the national database. Healthcare providers should further standardize healthcare data to facilitate interoperability.


Alteri, A. M. (2020). Data compatibility issues: How to prevent miscoding and dropped observations when using U.s. office of Personnel Management data sets. Review of Public Personnel Administration40(4), 743–753.×20904998

Payne, T. H., Lovis, C., Gutteridge, C., Pagliari, C., Natarajan, S., Yong, C., & Zhao, L.-P. (2019). Status of health information exchange: a comparison of six countries. Journal of Global Health9(2), 0204279.

Rafi, Z., & Greenland, S. (2020). Semantic and cognitive tools to aid statistical science: replace confidence and significance by compatibility and surprise. BMC Medical Research Methodology20(1), 244.

Shary, S. P. (2020). Weak and strong compatibility in data fitting problems under interval uncertainty. Advances in Data Science and Adaptive Analysis12(01), 2050002.