HIM 540 Module 5 Final Project Milestone Two Example

Final Project Milestone Two: Collect It Once, Define It Once, Healthcare Data Collection at Tidewater Crossing Health

[Student Name]

Southern New Hampshire University

HIM 540: Health Information Governance

Final Project Milestone Two

[Instructor Name]

[Date]

The organization, setting and figures below are a composite written as a model document. No real employer, client, colleague or patient is described.

Final Project Milestone Two: Collect It Once, Define It Once, Healthcare Data Collection at Tidewater Crossing Health

Governance cannot fix data after the fact as easily as it can shape how data are collected. Tidewater Crossing Health's reports suffer because the four hospitals capture the same facts in different fields, formats and vocabularies on two record platforms. This milestone plans data collection for the system: what data must serve, where they come from, how they are captured, which standards apply, how each element is defined and how quality is checked when data are entered rather than months later.

What the Data Must Serve

The council identified five purposes that drive data collection: direct patient care, payment and coding, quality reporting to regulators and payers, population health programs for the system's accountable care contracts and research and planning. Each purpose sets requirements. Quality reporting needs precise, consistent definitions; population health needs complete demographic and social needs data; research needs data that can be traced to their source. Weiskopf and Weng (2013) showed that the quality of record data can only be judged relative to its intended use, which is why the plan names purposes first.

Sources and Collection Methods

Data come from the two record platforms, the claims system, laboratory and pharmacy systems, state registries and patient-reported surveys. Collection methods vary by element. Most clinical facts should be captured as structured data at the point of care, such as vital signs, problem list entries and medication orders. Some information lives only in narrative notes and requires abstraction by trained staff or natural language processing. Identifying a group of patients, such as those with heart failure, often requires combining several sources; Hripcsak and Albers (2013) described how record data reflect care processes as much as disease, so reliable cohorts depend on explicit rules that combine diagnoses, medications and results. The plan documents the rule for every cohort the system reports.

Standard Vocabularies

To make data from two platforms comparable, the system will capture each type of data in a standard vocabulary: problems and findings in SNOMED CT, laboratory results and other observations in LOINC, drugs in RxNorm, ICD-10-CM and ICD-10-PCS for billing diagnoses and procedures and CPT for outpatient services. Local codes will be mapped to these standards, with the mapping maintained by the relevant domain steward. The standards also align with the federal core data set that certified systems must exchange, which prepares the ground for Milestone Three.

Linking Records Across Platforms

Data from two record platforms can only be combined if the system knows which records belong to the same patient. The plan relies on an enterprise master patient index that assigns each person one system identifier, linked to the medical record numbers used by each platform. Every data element that feeds a system report must carry that identifier, and records that cannot be linked with confidence are routed to health information staff for review rather than merged automatically. The patient identity cleanup chosen as a first-year priority in Milestone One is therefore not a separate project but a precondition for reliable data collection, since duplicate and mismatched records would make even perfectly defined measures count the wrong patients.

A Shared Data Dictionary

Every data element used in system reports will have an entry in a shared dictionary that states its name, definition, source, format, allowed values, steward and quality rules. Table 1 shows the model entry for the readmission measure adopted by the council, the element whose four local definitions prompted governance.

Table 1. Data Dictionary Entry: 30-Day All-Cause Readmission

Note. Entry drafted by the author and approved by the data governance committee.

Checking Quality at Collection

Kahn et al. (2016) proposed a harmonized framework for data quality built on three categories: conformance to formats and standards, completeness and plausibility. The plan applies each at the point of entry. Conformance checks reject values in the wrong format, such as a date in a free-text field. Completeness checks require key fields before a record can be signed, such as discharge disposition. Plausibility checks warn when values fall outside believable ranges, such as a weight of 900 pounds. Warnings are kept few and specific to avoid overwhelming staff, and each check is owned by a steward who reviews how often it fires.

Demographic and Social Needs Data

The four hospitals collect race and ethnicity using different screens and categories, and one still lets registration staff fill them in by observation. The plan adopts one approach: patients report their own race and ethnicity using categories consistent with current federal standards, which since 2024 use a combined question, with the option to decline. Social needs, such as food insecurity, housing instability and transportation barriers, will be collected with one validated screening tool at primary care and admission, coded so they can be counted. Gottlieb et al. (2015) argued that bringing social determinants into medical records requires standard, structured collection so that data can guide care and be analyzed, rather than remaining buried in notes.

Stewardship and Review

Each domain steward reviews the quality of their elements monthly and reports to the data governance committee quarterly. New data elements require a dictionary entry before they appear in any system report. The dictionary is published on the system's intranet so that anyone building a report uses the same definitions.

Conclusion

Collecting data once and defining it once is the most efficient form of data governance. By tying collection to purposes, standard vocabularies, a shared dictionary, quality checks at entry and standardized demographic and social needs data, Tidewater Crossing can produce reports that mean the same thing at every hospital. Milestone Three will address how those data move to and from other organizations.

References

Gottlieb, L. M., Tirozzi, K. J., Manchanda, R., Burns, A. R., & Sandel, M. T. (2015). Moving electronic medical records upstream: Incorporating social determinants of health. American Journal of Preventive Medicine, 48(2), 215-218. https://doi.org/10.1016/j.amepre.2014.07.009

Hripcsak, G., & Albers, D. J. (2013). Next-generation phenotyping of electronic health records. Journal of the American Medical Informatics Association, 20(1), 117-121. https://doi.org/10.1136/amiajnl-2012-001145

Kahn, M. G., Callahan, T. J., Barnard, J., Bauck, A. E., Brown, J., Davidson, B. N., Estiri, H., Goerg, C., Holve, E., Johnson, S. G., Liaw, S.-T., Hamilton-Lopez, M., Meeker, D., Ong, T. C., Ryan, P., Shang, N., Weiskopf, N. G., Weng, C., Zozus, M. N., & Schilling, L. (2016). A harmonized data quality assessment terminology and framework for the secondary use of electronic health record data. eGEMs, 4(1), Article 1244. https://doi.org/10.13063/2327-9214.1244

Weiskopf, N. G., & Weng, C. (2013). Methods and dimensions of electronic health record data quality assessment: Enabling reuse for clinical research. Journal of the American Medical Informatics Association, 20(1), 144-151. https://doi.org/10.1136/amiajnl-2011-000681