HIM 680 Module 3 Final Project Milestone One Example

Final Project Milestone One: Collected but Not Governed. An Assessment of Social Risk Data Governance at Cedar Prairie Health

[Student Name]

Southern New Hampshire University

HIM 680: Advanced Topics in HIM I

Final Project Milestone One

[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 One: Collected but Not Governed. An Assessment of Social Risk Data Governance at Cedar Prairie Health

Cedar Prairie Health, a composite system of four hospitals and 22 clinics in eastern Iowa, collects a great deal of information about patients' social needs. It does not govern it. Nobody is accountable for its accuracy, the term food insecure means different things in different reports and much of what is collected never reaches the data warehouse. This milestone assesses the current state of social risk data governance as the starting point for a data governance program. It inventories the data elements, maps who touches each, examines definitions and data quality, rates the maturity of current governance and ranks the gaps the program must address.

Approach

The assessment drew on four sources over five weeks: the electronic record's build documentation for the social needs screener and related fields, a query of twelve months of data in the record and the warehouse, interviews with nine people who capture or use the data and a review of every report that mentions social needs. The DAMA body of knowledge frames governance as decision authority over data assets and advises starting with an inventory of what exists and who is responsible (DAMA International, 2017). That framing guided the questions asked in each interview: what the data element means to you, where you get it, what you do with it and who you would call if it looked wrong.

Data Element Inventory

Table 1 lists the six data elements that make up the social risk domain today, where each originates, where it is stored and who uses it. The final column records whether anyone is formally responsible.

Table 1. Social Risk Data Elements at Cedar Prairie Health

Note. Compiled from build documentation, data queries and interviews, June 2026.

Definitions

The term food insecure has at least three working definitions in the organization. The screener logic flags a patient as positive if either of two food questions is answered sometimes true or often true. The population health team's dashboard counts a patient as food insecure only if the flag was set in the past six months. The equity committee's report counts any patient with a Z59 code in the past year. In a test of 1,000 clinic patients, the three definitions identified 164, 131 and 9 patients respectively. Each number is correct under its own definition, and each has been presented to leaders as the system's food insecurity count. Without an agreed definition and a named owner for it, the organization cannot answer a basic question about its own patients.

Data Quality

Quality was assessed with the framework of Kahn et al. (2016), whose three headings cover adherence to formats and code lists, gaps where values should exist and values that defy belief. On conformance, screener answers use locally invented codes rather than a standard terminology, and 4% of records store free text in fields meant for coded answers. On completeness, 71% of eligible clinic visits had a completed screener, but only 38% of positive screens led to a problem list entry, and fewer than 3% of hospital admissions of patients with a positive screen in the prior six months carried any social risk code. On plausibility, some clinics reported positive rates below 5% while others in similar neighborhoods reported above 25%, a pattern that suggests differences in how the questions are asked rather than in need. These findings echo the national pattern of low social risk coding reported by Truong et al. (2020).

Governance Maturity

Using a simple five-level scale, from ad hoc through defined, managed and measured to optimized, social risk data governance at Cedar Prairie is at the first level. Activity exists, and some of it is good, but it depends on individuals rather than roles, definitions and processes. The informal ownership of codes by the coding manager is the only exception, and even there no one measures whether codes are captured. Gottlieb et al. (2016) observed that social data efforts tend to focus on screening individuals rather than on extracting data that can support population health, and that observation fits: Cedar Prairie built screening carefully but never built the path from screening to usable data.

What Users Need

Interviews showed that users want the same data for different reasons, which is why a single definition must be chosen deliberately. The population health team needs a patient-level flag refreshed monthly to target outreach. The Medicaid health plan wants screening rates and positive rates by clinic each quarter in a standard format. The equity committee wants trends by race, language and neighborhood. Hospital case managers want to see a positive screen at admission so they can plan discharge. No current source serves all four, and each group has built a workaround that adds to the inconsistency.

Ranked Gaps

Five gaps were ranked by a working group of the director, the population health manager and the coding manager, weighing risk to decisions, number of users affected and ease of correction. First, there is no single definition for core measures such as food insecurity and housing instability. Second, no element has a named owner or steward. Third, screener answers are stored in local codes and never loaded to the warehouse. Fourth, coders cannot see clinic screening results during hospital coding. Fifth, referral outcomes are not returned from community agencies in usable form. The first two are governance gaps and come first, because fixing the technical gaps without them would simply move undefined data faster.

Conclusion

Cedar Prairie collects social risk data diligently and governs it hardly at all. The inventory, definitions test, quality review and maturity rating point to the same conclusion: the system needs owners, definitions and standards before it needs new tools. Milestone Two will design the governance framework to provide them.

References

DAMA International. (2017). DAMA-DMBOK: Data management body of knowledge (2nd ed.). Technics Publications.

Gottlieb, L., Tobey, R., Cantor, J., Hessler, D., & Adler, N. E. (2016). Integrating social and medical data to improve population health: Opportunities and barriers. Health Affairs, 35(11), 2116-2123. https://doi.org/10.1377/hlthaff.2016.0723

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

Truong, H. P., Luke, A. A., Hammond, G., Wadhera, R. K., Reidhead, M., & Joynt Maddox, K. E. (2020). Utilization of social determinants of health ICD-10 Z-codes among hospitalized patients in the United States, 2016-2017. Medical Care, 58(12), 1037-1043. https://doi.org/10.1097/MLR.0000000000001418