PHLT 8270 Week 3 Surveillance Data Flow Map Example

Following One Fact Through Five Systems: A Data Flow Map of the Carbapenemase Test Result From a Hospital Laboratory Bench to a Composite State's Surveillance Database

Student Name

Walden University

PHLT 8270: Health Informatics and Surveillance

Instructor Name

Month Day, Year

Following One Fact Through Five Systems: A Data Flow Map of the Carbapenemase Test Result From a Hospital Laboratory Bench to a Composite State's Surveillance Database

Why One Data Element

A data flow map can try to show everything a surveillance system receives, or it can follow one fact closely enough to see where it breaks. This map takes the second approach. The fact it follows is the result of a carbapenemase test: whether a carbapenem-resistant isolate produces an enzyme that can spread resistance. In the composite state's surveillance program, that single result decides whether an isolate becomes a confirmed carbapenemase-producing case and whether a containment response begins, so any point where it is lost, delayed or garbled changes what the program does. Dixon et al. (2013) describe how the quality of electronic data used for surveillance depends on every system it passes through, and this map traces those systems for one element.

The Five Systems

Table 1

Path of the Carbapenemase Test Result From Bench to Database

Note. The composite state receives electronic laboratory reports from most hospital laboratories and paper or fax reports from a few small laboratories.

Where the Fact Is Most Often Lost

At the Bench

The result exists only if someone performs the test. Some laboratories test every carbapenem-resistant isolate; others test only on request or not at all. A decision made at the bench, often by protocol and sometimes by workload, determines whether the fact ever comes into being.

In the Laboratory Information System

Many laboratories record the carbapenemase result as a comment attached to the susceptibility report, such as "carbapenemase detected," rather than as a coded result with its own observation code. Comments may display on the clinician's report but be dropped by the interface that builds the electronic message to the state.

In the Message

Moving from paper to electronic laboratory reports brought large gains; in one comparison, automated electronic reports identified 4.4 times as many cases as spontaneous paper reports and did so about eight days earlier (Overhage et al., 2008). But a message carries only what its builders mapped. In the composite state, several hospital interfaces send the organism identification and the antibiotic susceptibility results but not the carbapenemase observation, so the state sees a resistant organism with mechanism unknown even when the hospital knows the answer.

Between Two Laboratories

When the state laboratory performs molecular testing on a referred isolate, its result returns through the public health laboratory's own information system. Linking it to the hospital's original report depends on a specimen identifier that hospital staff must write on the referral form. Missing or mistyped identifiers leave two unlinked records, one with clinical details and one with the mechanism.

What the Map Shows the Program

Three conclusions follow. First, an "unknown" mechanism in the database has at least four possible causes: no test was performed, the result was recorded as free text and dropped, the interface omitted it or the state's result never linked. These causes call for different remedies, and the database cannot tell them apart. Second, the most effective repair is probably at the interface, where mapping the carbapenemase observation to a standard code would carry results the hospitals already produce. Third, the referral form's specimen identifier deserves the same attention as any laboratory test, because a failed link wastes a completed molecular result.

A Second Element the Map Touches

Following the carbapenemase result also exposed a second element that travels with it and fails in similar ways: the facility where the patient lives. A nursing home resident whose specimen is collected during a hospital visit may be reported with the hospital as the ordering facility and no record of the nursing home at all. The containment response then goes to the hospital, which may have discharged the patient, while the nursing home, where other residents are exposed, never hears. The facility of residence crosses the same five systems as the carbapenemase result, and it is lost at the same points: never captured at registration, recorded in a free-text field, omitted from the message or left unmatched at the state. Any repair to the carbapenemase result's path should carry this field with it.

Who Owns Each Step

The map also shows that no single organization controls the whole path. The hospital laboratory owns the bench and its information system. The hospital's information technology staff or a vendor owns the interface. The state public health laboratory owns molecular testing and its own system. The state surveillance program owns the database. A failure at any step is visible mainly to the state, at the end of the path, yet the state controls only the last step. That imbalance explains why errors persist: the people who could fix them rarely see their effects. A practical response is to send each laboratory a quarterly feedback report showing how many of its carbapenem-resistant isolates arrived with a carbapenemase result, how many arrived with mechanism unknown and how many referred isolates failed to link. Feedback of this kind turns the state's view of the end of the path into information the owners of earlier steps can act on.

Why It Matters for Containment

Containment efforts for carbapenemase producers depend on fast confirmation followed by screening of patients who shared a unit with the case; in national data, such screening found carriers among about one in nine people tested (Woodworth et al., 2018). Every day the fact spends stuck between systems is a day those contacts go unscreened. The map therefore points to a practical priority: shorten the path for the one result that triggers action.

How Technical the Map Needs to Be

The map names message standards and system types but avoids detailed field specifications, because its audience is the program's epidemiologists and managers, who need to know where the fact can fail and whom to call. A companion document for the informatics team would list the specific message segments and codes involved.

References

Dixon, B. E., Siegel, J. A., Oemig, T. V., & Grannis, S. J. (2013). Electronic health information quality challenges and interventions to improve public health surveillance data and practice. Public Health Reports, 128(6), 546-553. https://doi.org/10.1177/003335491312800614

Overhage, J. M., Grannis, S., & McDonald, C. J. (2008). A comparison of the completeness and timeliness of automated electronic laboratory reporting and spontaneous reporting of notifiable conditions. American Journal of Public Health, 98(2), 344-350. https://doi.org/10.2105/AJPH.2006.092700

Woodworth, K. R., Walters, M. S., Weiner, L. M., Edwards, J., Brown, A. C., Huang, J. Y., Malik, S., Slayton, R. B., Paul, P., Capers, C., Kainer, M. A., Wilde, N., Shugart, A., Mahon, G., Kallen, A. J., Patel, J., McDonald, L. C., Srinivasan, A., Craig, M., & Cardo, D. M. (2018). Vital signs: Containment of novel multidrug-resistant organisms and resistance mechanisms, United States, 2006-2017. MMWR. Morbidity and Mortality Weekly Report, 67(13), 396-401. https://doi.org/10.15585/mmwr.mm6713e1