HIM 680 Module 7 Final Project Milestone Three Example

Final Project Milestone Three: From Governed to Used. A Data Management and Informatics Plan for Cedar Prairie's Screening Data

[Student Name]

Southern New Hampshire University

HIM 680: Advanced Topics in HIM I

Final Project Milestone Three

[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 Three: From Governed to Used. A Data Management and Informatics Plan for Cedar Prairie's Screening Data

Governance decides what data mean and who is responsible for them. Data management and informatics decide how the data are stored, moved, protected and delivered. At Cedar Prairie Health, the composite Iowa system whose social risk data were found ungoverned in Milestone One and given a framework in Milestone Two, the delivery problem is as serious as the governance problem: screening answers never reach the warehouse, coders cannot see them and the Medicaid health plan receives a spreadsheet built by hand. This milestone plans the delivery. It begins with users, because the DAMA body of knowledge treats data management as a service to the people who use data, not as an end in itself (DAMA International, 2017).

Users and What They Need

Four user stories drive the design. A hospital coder needs to see, while coding an admission, whether the patient had a positive social needs screen in the prior six months, so that a social risk code can be assigned when the record supports it. A population health analyst needs a monthly patient-level table of food, housing and transportation status using the council's definitions, so that outreach can be targeted. The Medicaid plan's quality team needs a quarterly file of screening and positive rates by clinic in a standard format. The equity committee needs trends by race, ethnicity, language and neighborhood, with small numbers suppressed. Each story names a user, a need, a timing and a definition.

Warehouse Model

The warehouse will add a screening fact table with one row per answered question per visit, holding the LOINC question and answer codes, the visit date, the clinic and a patient key. Dimension tables will hold patient demographics, clinic and date. A derived table will calculate each patient's current status for food, housing and transportation using the council-approved definitions, so that every report uses the same logic rather than its own. Problem list entries, social risk codes from encounters and referral outcomes will be loaded into related tables linked by patient and date. Storing answers at the question level, rather than only the positive flag, lets analysts recalculate status if a definition changes.

Integration and Reconciliation

The nightly extract will add the screener flowsheet, which Module Six found had never been extracted. Each load will run a reconciliation check comparing the number of answers recorded in the source the previous day with the number loaded, and the warehouse lead will be alerted if they differ by more than half a percent. The local answer codes will be mapped to LOINC during loading, with the mapping table maintained by the screening steward under change control. A small monthly sample of records will be compared directly with the source as a validation check.

Coder Worklist

The most important informatics change sits in the hospital, not the warehouse. When a patient is admitted, a rule will look back half a year for a positive screen and, when one exists, add a note to the coder's worklist and to the clinical documentation specialist's queue. Coders can then assign a social risk code if the record supports it, and documentation specialists can prompt clinicians to address the need during the stay. Truong et al. (2020) reported that national hospital claims almost never carry these codes, and the chief reason at Cedar Prairie is simply that coders never see the screening result.

The rule will be tested for two weeks in one hospital before it goes live everywhere, with coders asked whether the alerts are useful or simply noise, since an ignored alert would change nothing.

Exchange With the Health Plan

The quarterly file to the Medicaid plan will move from a hand-built spreadsheet to a standards-based extract. The federal core data set expects social determinants assessments, problems, goals and interventions to be exchangeable (Office of the National Coordinator for Health Information Technology, n.d.), and the record vendor supports FHIR interfaces that represent screening results as coded observations. Benson and Grieve (2021) describe FHIR's resource model as the modern way to exchange discrete clinical data through standard web interfaces. The first quarter will send the aggregate rates the plan requests, and patient-level exchange will follow only after the council and privacy office approve a data sharing agreement that limits use to care coordination.

Access and Privacy

Social risk data are sensitive. Access will be role-based: clinicians and care team members see patient-level data for their own patients, analysts see patient-level data in the warehouse under the system's analytics access policy, and committee reports show aggregates with counts under eleven suppressed. Housing and safety answers will be restricted further, visible only to the care team and social work. Every access to the patient-level tables will be logged and reviewed quarterly by the privacy office.

Build Sequence and Measures

The work runs over six months. Months one and two add the extract, the LOINC mapping and reconciliation. Months three and four build the derived status table, the population health table and the coder worklist rule. Months five and six replace the health plan spreadsheet and build the equity dashboard. Success will be measured by four indicators: daily reconciliation within half a percent, the dashboard showing all screened patients rather than three quarters, social risk codes on at least 15% of admissions with a recent positive screen within six months of the worklist going live and the health plan file delivered on time with no manual steps. Gottlieb et al. (2016) argued that extracting social data at population scale requires standardized methods; these measures show whether the methods are working. Each indicator has a named owner who reports it to the council.

Conclusion

With this plan, a food screening answer will flow from the clinic to the warehouse every night, appear on a coder's worklist at the next admission, inform outreach each month and reach the Medicaid plan in standard form each quarter, with access matched to sensitivity. All three milestones now feed the governance program proposal that ends the course, where they will be condensed for an executive reader.

References

Benson, T., & Grieve, G. (2021). Principles of health interoperability: FHIR, HL7 and SNOMED CT (4th ed.). Springer.

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

Office of the National Coordinator for Health Information Technology. (n.d.). United States Core Data for Interoperability (USCDI). https://www.healthit.gov/isp/united-states-core-data-interoperability-uscdi

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