HIM 220 Module 7 Project Two Example

Project Two: Fixing Data Where It Starts, a Data Quality Improvement Plan for Kettle River Health

[Student Name]

Southern New Hampshire University

HIM 220: Healthcare Data Management

Project 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.

Project Two: Fixing Data Where It Starts, a Data Quality Improvement Plan for Kettle River Health

Summary

Kettle River Health's data problems are measurable, and most begin where data are first recorded. This plan targets five problems identified in the Module Two assessment and assigns each to the governance roles defined in Module Six. Over twelve months it aims to cut suspected duplicates from 6.4% to below 2%, missing race and ethnicity from 19% to below 5% and unmapped laboratory results from 12% to below 2%, while raising problem list currency to 80% and eliminating implausible timestamps from reports. First-year costs are about $96,000.

Guiding Principles

Three principles shape the plan. Fix problems at the point of capture rather than cleaning them repeatedly downstream. Judge quality by fitness for use, as Kahn et al. (2016) recommend, so each target is tied to a purpose such as safety or equity reporting. And measure every problem with a repeatable method so progress can be seen, drawing on the dimensions Weiskopf and Weng (2013) described.

Problem 1: Duplicate Records

Target: suspected duplicates below 2% and confirmed overlays at zero. Actions: adopt the registration search standard requiring three search attempts with name variations; add photo capture at registration; standardize address and name formats; and fund a temporary merge specialist for six months to clear the 9,000-pair backlog. Grannis et al. (2019) found that standardizing fields improved matching sensitivity only modestly, so the plan relies on search behavior and photos as well. Owner: vice president of revenue cycle. Steward: HIM MPI coordinator.

Problem 2: Missing Race and Ethnicity

Target: missing below 5% at every site within twelve months. Actions: adopt one script and category list; let patients self-report on check-in tablets and in the portal; train registration staff, especially in the emergency department, to explain why the question matters; and add a completeness report by registrar. Polubriaginof et al. (2019) found that 86% of patients gave meaningful answers when self-reporting, which supports shifting collection toward patients. Owner: chief equity officer. Steward: equity office analyst.

Problem 3: Unmapped Laboratory Results

Target: unmapped reference laboratory results below 2%. Actions: map the 140 local test names now lacking codes, require LOINC codes in the reference laboratory contract renewal and set the interface to hold unmapped results in a review queue rather than loading them silently. McDonald et al. (2003) described LOINC as the means by which results from different laboratories are recognized as the same test, so mapping restores those results to dashboards and registries. Owner: chief medical information officer. Steward: laboratory information system analyst.

Problem 4: Stale Problem Lists

Target: 80% of primary care patients with a problem list reviewed in the past year. Actions: add a problem list review step to annual wellness and chronic care visit templates, give medical assistants a pre-visit checklist that flags resolved conditions for clinician review and share clinic-level currency rates monthly. Owner: vice president of ambulatory care. Steward: primary care nurse informaticist.

Problem 5: Implausible Timestamps

Target: no negative lengths of stay or stays over 400 days reaching reports without review. Actions: add warehouse plausibility checks that route suspect records to the nursing informatics team within 48 hours and revise downtime procedures so staff record actual times on paper forms for later entry. Owner: chief nursing officer. Steward: nursing informatics lead.

The Quarterly Scorecard

Stewards will report each measure quarterly to the governance committee. Table 1 lists baselines, targets and measurement methods.

Table 1. Data Quality Scorecard

Note. Baselines from the Module Two assessment.

Timeline

Months one and two focus on approving the search and demographic standards and hiring the merge specialist. Months three to six launch self-reporting tablets, laboratory mapping and warehouse plausibility checks. Months six to nine add problem list template changes and registrar-level reports. Months ten to twelve evaluate results and set second-year targets.

Budget

First-year costs total about $96,000: $38,000 for a six-month merge specialist, $22,000 for photo capture at registration desks, $14,000 for check-in tablet configuration and self-reporting screens, $12,000 for laboratory mapping support from the vendor, $6,000 for training time and $4,000 for reporting development. Savings are harder to count but real: fewer repeated tests from split records, less analyst time reconciling reports and fewer rejected exchange transactions.

Engaging the People Who Create the Data

Registration staff, medical assistants and nurses create most of the data this plan improves. Each group will help design the changes that affect them, receive feedback on their own performance rather than only system averages and see how better data help patients, such as a duplicate that hid an allergy. Early wins will be publicized, and the governance committee will recognize high-performing sites.

Risks

Registration may slow as staff search more carefully, so the search standard will be tested at two desks before rollout. Patients may worry about why race and ethnicity are asked, so scripts will explain the purpose and allow a declined answer. The reference laboratory may resist contract changes, so the mapping team will prepare codes in advance to reduce its burden.

Evaluation

At twelve months, the plan will be judged on whether scorecard targets were met, whether the readmission and equity dashboards changed as data improved and whether registration times rose by more than a minute on average. A brief survey of stewards will assess whether the governance process supported or slowed their work.

Conclusion

Each of Kettle River's data problems has a measurable target, an owner, a steward and a fix at the point of capture. With a quarterly scorecard, a modest budget and front-line staff involved in the changes, the system can make its dashboards trustworthy and its data fit for the uses that matter most: safety, equity and sound decisions.

References

Grannis, S. J., Xu, H., Vest, J. R., Kasthurirathne, S., Bo, N., Moscovitch, B., Torkzadeh, R., & Rising, J. (2019). Evaluating the effect of data standardization and validation on patient matching accuracy. Journal of the American Medical Informatics Association, 26(5), 447-456. https://doi.org/10.1093/jamia/ocy191

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 18. https://doi.org/10.13063/2327-9214.1244

McDonald, C. J., Huff, S. M., Suico, J. G., Hill, G., Leavelle, D., Aller, R., Forrey, A., Mercer, K., DeMoor, G., Hook, J., Williams, W., Case, J., & Maloney, P. (2003). LOINC, a universal standard for identifying laboratory observations: A 5-year update. Clinical Chemistry, 49(4), 624-633. https://doi.org/10.1373/49.4.624

Polubriaginof, F. C. G., Ryan, P., Salmasian, H., Shapiro, A. W., Perotte, A., Safford, M. M., Hripcsak, G., Smith, S., Tatonetti, N. P., & Vawdrey, D. K. (2019). Challenges with quality of race and ethnicity data in observational databases. Journal of the American Medical Informatics Association, 26(8-9), 730-736. https://doi.org/10.1093/jamia/ocz113

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