HIM 680 Module 9 Final Project Example

Final Project: Counting Who Needs Help. A Data Governance Program for Social Needs Information in Eastern Iowa

[Student Name]

Southern New Hampshire University

HIM 680: Advanced Topics in HIM I

Final Project

[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: Counting Who Needs Help. A Data Governance Program for Social Needs Information in Eastern Iowa

Summary

Cedar Prairie Health screens about 9,000 clinic patients a month for food, housing and transportation needs. None of those answers reach the data warehouse by design, the system's leaders have been shown three different food insecurity counts and hospital coders see screening results so rarely that social risk codes appear on fewer than 3% of admissions of patients known to be at risk. This proposal asks the chief medical officer to sponsor a twelve-month data governance program for social risk data. It establishes owners and stewards, one evidence-based definition per core measure, standard terminologies, quality targets and a delivery plan that puts the data in front of coders, analysts and the Medicaid health plan. First-year cost is about $96,000, almost entirely staff time.

The Case for a Program

The assessment found six social risk data elements with one informal owner among them, three working definitions of food insecurity that produced counts of 164, 131 and 9 in the same 1,000 patients, screener answers stored in local codes, positive rates across similar clinics ranging from 4% to 27% and a nightly extract that had never included the screener. The pattern is not unique. Nationally, social risk codes appeared on fewer than 2% of hospitalizations in a study of more than 14 million stays (Truong et al., 2020), and Gottlieb et al. (2016) observed that health systems have invested in screening individual patients far more than in turning the results into data that can support population health. Cedar Prairie built screening well. It never built governance or delivery.

Governance Structure

A small council will hold three decision rights: approving definitions, approving changes to capture or calculation and settling disputes between users. The population health director will chair it as data owner. Three stewards will serve, each with about two hours a week written into their job: the clinic social work lead for screening and problem list data, the coding manager for social risk codes and the warehouse lead for calculations. Information technology remains custodian. The chief medical officer is executive sponsor and the final point of escalation. This follows the DAMA body of knowledge's separation of accountability for meaning from custody of systems (DAMA International, 2017), sized for an organization governing its first domain.

Standards

The council's first decision will define food insecurity from the brief food screen tested by Hager et al. (2010), which identified food-insecure families with high sensitivity; a patient's latest screen in the past year counts as positive when either answer is often or sometimes true, reported per patient screened. Housing instability and transportation need will be defined the same way from their screener items. Screening answers will be stored with LOINC codes, clinician-confirmed needs as SNOMED CT problem list findings and claim-level needs as ICD-10-CM Z codes, linked through the record. Every report shown to leaders or outside parties must name the definition it uses.

Quality Targets

Quality will be measured monthly using the conformance, completeness and plausibility categories of Kahn et al. (2016). Table 1 shows the baselines and twelve-month targets.

Table 1. Quality Baselines and Twelve-Month Targets

Note. Baselines from the Milestone One assessment and the Module Six lineage trace.

Delivery Plan

Governed data are useful only when they reach users. The nightly extract will add the screener, with a reconciliation check that alerts the warehouse lead if loaded answers differ from source answers by more than half a percent. A screening fact table will store answers at the question level, and a single derived table will apply the council's definitions so every report uses the same logic. A worklist rule will show coders and documentation specialists any positive screen in the six months before an admission. The Medicaid plan's quarterly file will be produced by a standard extract instead of by hand, aggregate first and patient level only under an approved data sharing agreement. The equity committee will receive trends by demographic group with small counts suppressed.

Privacy and Trust

Social risk answers are sensitive, and their quality depends on whether patients feel safe giving them. Most patients in a multi-site survey found screening appropriate, but comfort with documentation was lower among those who had experienced discrimination in health care (De Marchis et al., 2019). The program will therefore restrict housing and safety answers to the care team and social work, log access to patient-level tables, offer private tablet screening in every clinic and add a short explanation before the questions. The share of screens completed privately will be tracked alongside the quality measures, because a low positive rate can signal distrust as easily as low need.

Roadmap and Budget

The roadmap has three phases. By the end of month three, the council is chartered, the food insecurity definition is approved, the extract and reconciliation go live and local codes are mapped to LOINC. In months four to eight, the derived status table, coder worklist and population health table are built and the tablet workflow spreads to all clinics. In months nine to twelve, the health plan file and equity dashboard are completed and the council reviews a full year of quality data. First-year costs are about $96,000: steward time valued at $31,000, analyst and warehouse development at $38,000, interface and worklist configuration at $15,000, tablets for clinics without them at $9,000 and training at $3,000.

Risks

Three risks stand out. The equity committee may resist retiring its code-based count; the sponsor's endorsement and a clear rule that the code count becomes a capture measure address this. Coders may ignore worklist alerts; a two-week pilot and coder feedback before full rollout reduce that risk. Stewards may lose time to other duties; written job duties and twice-yearly review by the owner protect the roles.

Request

The proposal asks the chief medical officer to sponsor the program, to approve the council's charter and its three decision rights, to authorize the steward time and the first-year budget and to receive a quarterly report on the six quality measures. With that support, Cedar Prairie can know, within a year, how many of its patients need help with food, housing and transportation, and can show that the number is right.

References

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

De Marchis, E. H., Hessler, D., Fichtenberg, C., Adler, N., Byhoff, E., Cohen, A. J., Doran, K. M., Ettinger de Cuba, S., Fleegler, E. W., Lewis, C. C., Lindau, S. T., Tung, E. L., Huebschmann, A. G., Prather, A. A., Raven, M., Gavin, N., Jepson, S., Johnson, W., Ochoa, E., . . . Gottlieb, L. M. (2019). Part I: A quantitative study of social risk screening acceptability in patients and caregivers. American Journal of Preventive Medicine, 57(6, Suppl. 1), S25-S37. https://doi.org/10.1016/j.amepre.2019.07.010

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

Hager, E. R., Quigg, A. M., Black, M. M., Coleman, S. M., Heeren, T., Rose-Jacobs, R., Cook, J. T., de Cuba, S. A. E., Casey, P. H., Chilton, M., Cutts, D. B., Meyers, A. F., & Frank, D. A. (2010). Development and validity of a 2-item screen to identify families at risk for food insecurity. Pediatrics, 126(1), e26-e32. https://doi.org/10.1542/peds.2009-3146

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