| Course | DPH 850 Health Informatics for Public Health Leaders |
|---|---|
| Module | Module 6 |
| Paper type | System evaluation paper |
| Length | About 1,101 words, 7 pages |
| Format | APA 7 student paper |
| School | Aspen University |
| Program | Doctor of Public Health |
| Updated | September 2026 |
Free sample paper for DPH 850 Module 6
Did It Work? A First-Year Evaluation of a State Disease Surveillance System
Student Name
Doctor of Public Health Program, Aspen University
DPH 850: Health Informatics for Public Health Leaders
Instructor Name
Month Day, Year
Did It Work? A First-Year Evaluation of a State Disease Surveillance System
Evaluation tells leaders whether an information system is delivering what it promised and where it needs improvement. This paper evaluates a composite state health department's new disease surveillance system one year after statewide launch, using an information systems success model and attributes commonly used to judge public health surveillance.
Evaluation Questions
The evaluation asked four questions. Has the system improved the timeliness and completeness of case reporting? Do users find it usable and useful? Has it reduced manual work? And what problems remain that should guide the next phase? These questions were agreed with state and local users before data collection began.
Frameworks
The model of DeLone and McLean (2003) links system quality, information quality and service quality to use and user satisfaction, which in turn produce net benefits. Public health surveillance evaluation adds attributes such as simplicity, flexibility, data quality, acceptability, sensitivity, representativeness, timeliness and stability. Together they cover both the technology and its public health purpose.
Measures and Results
The table summarizes selected measures, baseline values from the old system and first-year results.
| Dimension | Measure | Baseline | Year one |
|---|---|---|---|
| System quality | Uptime | 96.8% | 99.6% |
| Information quality | Race and ethnicity complete in case reports | 58% | 64% |
| Timeliness | Median days from specimen to case creation | 3.1 | 1.2 |
| Use | Share of case reports processed electronically | 70% | 91% |
| User satisfaction | Users rating system good or excellent | 38% | 71% |
| Net benefit | Staff hours per week on manual entry | 420 | 95 |
Methods
The evaluation used system log data for uptime, volume and timeliness; an audit of 400 randomly selected case records for completeness and accuracy; a survey of 612 state and local users with a 64% response rate; and interviews with 24 users and laboratory partners. Costs came from project financial records. Audit reviewers compared each record with the original laboratory or clinic report.
Timeliness and Workload
The system cut the median time from specimen collection to case creation from 3.1 days to 1.2, largely through automated laboratory and case report intake. Manual entry time fell by about three-quarters, freeing staff for investigation and analysis. These are the net benefits the project was designed to deliver.
Data Completeness
Completeness of race and ethnicity improved only modestly, from 58% to 64%, because many laboratory reports do not carry these fields and electronic case reports have not yet reached all clinics. The audit also found that address errors sent about 2% of cases to the wrong local health department at first, a problem since reduced by improved geocoding.
User Experience
User satisfaction rose sharply, with 71% rating the system good or excellent. Users praised faster case assignment and fewer faxes. Complaints centered on slow report generation, confusing screens for rare diseases and limited training on analysis tools. Small rural departments reported more difficulty than large urban ones.
Laboratory and Clinic Partners
Laboratory partners reported smoother submission once interfaces were validated, though several small laboratories still submit by secure upload rather than automated messaging. Clinics reported that electronic case reporting reduced phone calls from investigators. Barriers similar to those found nationally, including limited technical capacity and variable requirements, slowed some connections (Holmgren et al., 2020).
Costs
First-year operating costs were $2.3 million, slightly above the $2.1 million estimate because of additional interface work. Against this, the reduction of about 325 staff hours per week in manual entry is valued at roughly $780,000 a year in staff time redirected to other work.
Sustained Use and Adoption
Adoption across local health departments was uneven, with a few still keeping paper logs alongside the system. Adoption frameworks suggest that complexity in organizations and the wider system can stall scale-up (Greenhalgh et al., 2017). Targeted support for these departments is planned.
Limitations
The evaluation compares one year of the new system with the final year of the old one, which differed in disease activity. User surveys may overrepresent engaged users. The audit sample is adequate for statewide estimates but not for small departments. These limits suggest caution in attributing all changes to the system.
Recommendations
Recommendations include extending electronic case reporting to remaining clinics, working with laboratories to transmit race and ethnicity, improving report speed and screens for rare diseases, offering advanced analysis training and targeting support to departments with low adoption. The evaluation should be repeated in year three.
Sharing Results
Findings will be presented to state and local users, laboratory partners and the budget office, with a short public summary. Sharing results openly builds trust and helps partners see how their participation improves the system.
Evaluation Design
A before-and-after comparison is the most feasible design for a statewide system, but it cannot rule out other influences on the measures. Where possible, the evaluation compared counties that went live earlier with those that went live later, which helps separate effects of the system from statewide trends.
Stakeholder Involvement
Evaluation questions and draft findings were reviewed with a group of state and local users and two laboratory partners. Their input sharpened questions, for example adding a measure of how often investigators must call clinics for missing data, and increased trust in the results.
Using Results
Evaluation should change something. The department has already funded additional electronic case reporting connections and scheduled training on analysis tools in response to findings. A follow-up review in six months will check whether these actions improved the targeted measures.
Equity in Evaluation
The evaluation examined whether improvements reached all parts of the state. Timeliness improved more in urban counties with large hospital laboratories than in rural counties relying on small laboratories and clinics. Race and ethnicity completeness remained lowest for reports from commercial laboratories. These patterns shape where support is directed next.
Evaluation Capacity
Few health departments have staff dedicated to evaluating their own information systems. Building this capacity, even a part-time evaluator working with the informatics team, allows problems to be caught early and improvements to be documented for funders.
Comparing With Other States
Several states using the same shared platform agreed to compare measures. The department's timeliness gains were similar to peers', but its race and ethnicity completeness was lower, pointing to a local data collection problem rather than a platform limitation. Peer comparison helps separate what the system can deliver from what depends on local partners and practices.
Conclusion
One year after launch, the new surveillance system has improved uptime, timeliness, electronic processing and user satisfaction and has sharply reduced manual work. Gaps remain in race and ethnicity completeness, rural adoption and reporting tools. Evaluation grounded in an information systems success model and surveillance attributes gives leaders clear evidence for the next phase.
References
DeLone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: A ten-year update. Journal of Management Information Systems, 19(4), 9-30. https://doi.org/10.1080/07421222.2003.11045748
Greenhalgh, T., Wherton, J., Papoutsi, C., Lynch, J., Hughes, G., A'Court, C., Hinder, S., Fahy, N., Procter, R., & Shaw, S. (2017). Beyond adoption: A new framework for theorizing and evaluating nonadoption, abandonment, and challenges to the scale-up, spread, and sustainability of health and care technologies. Journal of Medical Internet Research, 19(11), Article e367. https://doi.org/10.2196/jmir.8775
Holmgren, A. J., Apathy, N. C., & Adler-Milstein, J. (2020). Barriers to hospital electronic public health reporting and implications for the COVID-19 pandemic. Journal of the American Medical Informatics Association, 27(8), 1306-1309. https://doi.org/10.1093/jamia/ocaa112
Reading the DPH 850 Module 6 assignment instructions
Evaluating information systems is named in Aspen's DPH 850 catalog entry, and the sixth module's instructions are reserved for enrolled students, so this example evaluates a system after its first year. Evaluation papers usually ask for questions, a framework, measures, methods, findings, limitations and recommendations. Agree questions with users. Use a recognized framework. Report baseline and follow-up values in a table. Combine log data, audits and surveys. Report what did not improve as clearly as what did. Tie recommendations to findings and plan a follow-up. Report costs and staff time saved alongside technical measures. Say how often the evaluation will be repeated.
How this DPH 850 Module 6 example is built
The paper first sets evaluation questions and frameworks, then presents a four-column table of measures with baseline and year-one values. Methods, timeliness and workload, completeness, user experience, partners, costs, adoption, limitations, recommendations and sharing results follow. Evaluation design, stakeholder involvement, using results, equity, evaluation capacity and comparison with other states are added. A margin comment explains why questions were agreed with users first. The conclusion summarizes gains and remaining gaps. Findings follow the order of the evaluation questions, so readers can match each answer to the question that prompted it and see which remain open. The results table's six rows each receive a short discussion before recommendations are drawn. Recommendations are grouped by who must act.
Where the marks sit in the DPH 850 Module 6 rubric
Evaluation papers are marked on clear questions, an appropriate framework, sound methods, honest findings and actionable recommendations. This paper cites DeLone and McLean's success model, Greenhalgh and colleagues on adoption plus a study of hospital reporting obstacles, formatted in APA style. The results table shows change against baseline. Weak results are reported openly. Equity analysis by region and source shows depth. Using results to fund specific actions demonstrates that evaluation drives decisions. Its limitations section explains why a before-and-after design cannot prove every change came from the system, and the staggered rollout comparison partly addresses that weakness. Costs are set against staff time saved, giving a rough sense of value.
DPH 850 Module 6 help: mistakes that cost marks
Students often evaluate only whether a system works technically, or report only positive results. Measure use, satisfaction and net benefits too. Include a baseline. Describe your methods clearly. State limitations, especially for before-and-after designs. Compare with peers if you can. If choosing measures is difficult, a tutor can help you match each evaluation question to a measure and data source. Close with the one change you would fund first. Keep your evaluation questions to four or five, and make sure each has at least one measure and a data source you can actually obtain. Share draft findings with users before finalizing; they will spot errors and add context that numbers miss. Label every value with its time period.
Write yours, or have the desk draft it
This paper is an original model document written by our desk, not a submitted student paper and not an official Aspen University document. Read it for the moves, then write your own to the instructions in your classroom. If you want one built to your exact prompt and rubric, the first custom sample is free and arrives in 24 to 48 hours.
More DPH 850 and Doctor of Public Health sample papers
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- DPH 850 Module 2: Public Health Information Systems
- DPH 850 Module 3: Data Standards and Interoperability
- DPH 850 Module 4: Planning an Information System
- DPH 850 Module 5: Implementation and Change Management
- DPH 850 Module 7: Data Equity and Vulnerable Populations
- DPH 850 Module 8: Health Data Policy and Governance
- DPH 840 Module 6: Health Economics Applied to a Decision
- DPH 890 Module 5: An Executive Summary for Decision Makers
- DPH 801 Module 6: Theory-Based Intervention Design
- DPH 820 Module 8: Funding and Sustaining a Policy
DPH 850 Module 6 questions, answered
What does DPH 850 Module 6 usually ask for?
Aspen's DPH 850 covers evaluating information systems, so an evaluation of a public health information system is typical. Confirm with your classroom prompt.
What are surveillance system attributes?
Qualities such as simplicity, flexibility, data quality, acceptability, sensitivity, representativeness, timeliness and stability used to judge surveillance.
What is the DeLone and McLean model?
A model linking system, information and service quality to use, user satisfaction and net benefits.
Where can I find a free DPH 850 Module 6 sample paper?
Read the first-year evaluation above; its table compares baseline and year-one results across six dimensions.
How is an information system evaluated in DPH 850 Module 6?
By agreeing questions with users, applying a success model and surveillance attributes, measuring change from baseline and making recommendations.