DDHA 8703 Week 3 Data Source Review Example

Course	DDHA 8703 Advanced Health Analytics and Data-Driven Decision Making
Week	Week 3
Paper type	Data source review
Length	About 1,060 words, 6 pages
Format	APA 7 student paper
School	Walden University
Program	Doctor of Healthcare Administration (DHA)
Updated	October 2026

Free sample paper for DDHA 8703 Week 3

A Record Built to Get Paid: A Data Source Review of an Accountable Care Claims Extract, Field by Field, Before It Is Used to Find Patients With Poorly Controlled Diabetes

Student Name

Walden University

DDHA 8703: Advanced Health Analytics and Data-Driven Decision Making

Instructor Name

Month Day, Year

What this page is doingThe title names the purpose the data were created for and the purpose proposed for them, which is the gap the review examines.

A Record Built to Get Paid: A Data Source Review of an Accountable Care Claims Extract, Field by Field, Before It Is Used to Find Patients With Poorly Controlled Diabetes

The Proposed Use

Northgate Health, a composite health system, is accountable for the cost and quality of care for about 38,000 Medicare beneficiaries under a shared savings arrangement, and it receives a monthly claims extract for them. The population health team proposes to use this extract for two things: to identify patients with poorly controlled diabetes for outreach by care managers, and to measure whether patients discharged from any hospital are seen by a Northgate clinician within seven days. This review asks, before anyone builds either measure, what the extract was created to do and therefore what it can and cannot support.

Where the Data Come From

A claim exists to support payment. A provider submits it to show that a billable service was delivered to an eligible person, with enough information to determine the amount owed. Everything about its shape follows from that purpose. Diagnosis codes appear because they justify the service billed. Dates appear because payment depends on eligibility on a date. Clinical results do not appear, because no payment depends on them. Tyree et al. (2006), describing the use of insurance claims for utilization research, noted that claims capture billed services well but omit services not billed, care paid by other sources and clinical detail, and that their structure varies by payer and claim type. Iezzoni (1997), assessing quality measurement from administrative data, made the broader point that such data were built for billing and must be judged on that basis.

Field by Field

Table 1

Fields the Proposed Measures Would Rely On

Field	What it captures	What it does not capture	When it is populated	By whom \|
Beneficiary identifier	The person enrolled	Whether the person is still attributed next month	At enrollment	The payer \|
Diagnosis codes	Conditions recorded to justify each billed service	Whether the condition is controlled or severe	When the claim is submitted	Provider billing staff \|
Procedure codes	Services billed, including laboratory tests ordered	Test results	When the claim is submitted	Provider billing staff \|
Service dates	Date each service was delivered	Time of day	When the claim is submitted	Provider billing staff \|
Facility claim type	Inpatient, outpatient, skilled nursing and so on	Reason for admission beyond diagnosis codes	When the claim is submitted	Facility billing staff \|
Discharge date	End of an inpatient stay	Where the patient went if not coded	When the facility claim is submitted	Facility billing staff \|
Rendering provider	Clinician who billed the service	Whether that clinician is the patient's usual source of care	When the claim is submitted	Provider billing staff \|
Adjustment indicator	Whether a claim replaces an earlier one	The reason for the change	When an adjustment is processed	The payer \|

Fields are as described in the extract documentation provided to Northgate; timing reflects the review of six months of extracts.

Diagnosis Codes as Evidence About Billing

A diagnosis code in a claim records that a clinician or coder listed the condition to justify a service. It is good evidence that the patient has been treated for diabetes, since diabetes codes are rarely recorded without reason. It is weak evidence about whether diabetes is controlled. A code for diabetes with hyperglycemia may appear because a clinician chose a more specific code during one visit, while another clinician seeing a patient with a glucose over 300 may code diabetes without complications. O'Malley et al. (2005) trace coding error to documentation, coder judgment and the coding rules in force, all of which vary in ways unrelated to the patient's clinical state. The extract also contains no laboratory values. A procedure code shows that an A1c test was billed; it does not show the result.

Timing

Two timing problems matter. The first is lag. Claims arrive in the extract after they are paid, typically weeks to months after the service. In the six months reviewed, about half of outpatient claims arrived within a month of service and most within three months, but some inpatient claims arrived later still. The second is revision. Claims are adjusted after submission, for example when a facility corrects a code, and the extract delivers the adjustment as a new record that must replace the earlier one. An analyst who does not apply the adjustment indicator will count some services twice.

Who Is in the Extract

A further feature shapes every measure. The extract covers patients attributed to Northgate in a given month, and attribution is recalculated by the payer as patients' use of clinicians changes. In the six months reviewed, about 3% of beneficiaries entered or left the attributed population each month. A patient discharged in March may no longer be attributed by May, when the discharge claim arrives. Any measure built from the extract must fix its population at a stated point, or its denominator will drift even when care has not changed at all.

Two Questions the Source Can Answer

First, the extract can answer whether attributed patients with diabetes had an A1c test billed in the past year, since testing generates claims from any laboratory, including those outside Northgate. Second, it can answer, retrospectively, whether patients discharged from any hospital had an outpatient visit with a Northgate clinician within seven days, because both the discharge and the visit generate claims. The second answer will be about three months old by the time it is reliable.

Two Questions It Cannot Answer

The extract cannot identify patients whose diabetes is poorly controlled, because it contains no laboratory results and its codes are a poor proxy for control. That measure should be built from Northgate's own laboratory data, accepting that it will miss tests done elsewhere. The extract also cannot support real-time outreach after discharge, because the discharge claim arrives weeks after the patient has gone home. For that purpose, admission and discharge notifications from hospitals are the appropriate source.

Conclusion

The claims extract is a sound source for counting billed services across all providers and for retrospective measures of follow-up. It is the wrong source for judging disease control or triggering timely outreach. The population health team should use it for what payment data do well and pair it with clinical and notification data for the rest.

References

Iezzoni, L. I. (1997). Assessing quality using administrative data. Annals of Internal Medicine, 127(8, Pt. 2), 666-674. https://doi.org/10.7326/0003-4819-127-8_Part_2-199710151-00048

O'Malley, K. J., Cook, K. F., Price, M. D., Wildes, K. R., Hurdle, J. F., & Ashton, C. M. (2005). Measuring diagnoses: ICD code accuracy. Health Services Research, 40(5, Pt. 2), 1620-1639. https://doi.org/10.1111/j.1475-6773.2005.00444.x

Tyree, P. T., Lind, B. K., & Lafferty, W. E. (2006). Challenges of using medical insurance claims data for utilization analysis. American Journal of Medical Quality, 21(4), 269-275. https://doi.org/10.1177/1062860606288774

Reading the DDHA 8703 Week 3 instructions

Data source reviews in this course usually ask whether a particular dataset can support a proposed analysis. Begin with the origin of the data: who created the record, for what purpose and under what rules. Then judge each field the analysis would rely on, stating what it captures, what it misses, when it is populated and by whom. Pay special attention to fields whose meaning is shaped by their purpose, such as diagnosis codes recorded to justify payment. Flag timing problems, including lag and later revisions, because they determine whether a measure can be built at all. Close by naming questions the source can answer well and questions it cannot, and recommend a better source for the latter. A review that only describes fields without judging them against the intended use misses the assignment's purpose.

How this DDHA 8703 Week 3 example is built

The review opens with the population health team's two proposed uses. A section on origin explains that claims exist to support payment, citing Tyree and colleagues and Iezzoni. Table 1 covers eight fields, from beneficiary identifier to adjustment indicator, with what each does and does not capture and when it is populated. A section treats diagnosis codes as evidence about billing rather than disease control, drawing on O'Malley and colleagues and noting the absence of laboratory values. A timing section describes claims lag and adjustments that create duplicates if ignored. A section on attribution explains how the population shifts month to month. The review then names two answerable questions and two unanswerable ones and recommends laboratory data and admission notifications for the latter.

Reading the DDHA 8703 Week 3 rubric

Data source reviews are graded on whether the writer judges data against their intended use. Starting from the data's origin is expected and strengthens every later judgment. Field-level analysis that states both what is captured and what is not earns credit, as does attention to who populates each field and when. Markers look for recognition that codes recorded for payment are weak evidence about clinical states. Timing issues, including lag and revisions, are often explicit criteria. Clear statements of what the source can and cannot answer, with alternatives suggested, show practical judgment. Sources should be used to support claims about administrative data, and a well-organized table helps graders assess completeness.

Common DDHA 8703 Week 3 mistakes, and how to avoid them

Students often describe a dataset's contents without asking why it exists. Start with purpose, since it explains most of the data's shape. Another error is treating diagnosis codes as clinical facts; ask what process put them there. Check how late data arrive and whether records are revised, because both affect any measure. Look at who is included and whether that changes over time. Be specific about what cannot be answered, and suggest a better source rather than stopping at criticism. If you need a review of a dataset your organization plans to use, our writers can prepare it with a field-by-field table.

Get a DDHA 8703 Week 3 example written to your instructions

Send the Week 3 prompt and rubric and the desk returns a source review shaped to them, free the first time, inside 24-48 hours. The extract it appraises will be a described one. Real files, credentials or query access from your workplace are neither requested nor wanted here.

More DDHA 8703 papers and DHA samples

DDHA 8703 Week 3 questions, answered

Where can I find a free DDHA 8703 Week 3 sample paper?

The claims extract data source review, with its field table, is free to read on this page.

What data source does the DDHA 8703 Week 3 example review?

A monthly Medicare accountable care claims extract covering about 38,000 beneficiaries at a composite health system.

Why are diagnosis codes treated cautiously?

They are recorded to justify billed services, so they show treatment for a condition but say little about whether it is controlled.

Which questions can the extract answer?

Whether patients had an A1c test billed anywhere and, retrospectively, whether discharged patients were seen within seven days.

Which questions can it not answer?

Which patients have poorly controlled diabetes and which patients need a call right after discharge.