HLTH 5443 Module 2 Health Informatics Analysis Example

From Puffs to Decisions: How a School Asthma Program Can Turn Smart Inhaler Data Into Information for Care and Health Education

Student Name

American College of Education

HLTH5443: Technology, Leadership, and Health Informatics

Module 2 Assignment

Instructor Name

October 12, 2026

Introduction

Module 1 of this project recommended that a composite county school health program pilot clip-on inhaler sensors with about 60 teenagers who have persistent asthma. The sensors will produce a stream of data: a time stamp every time an inhaler is pressed. Data alone do not help anyone. They must be moved, checked, summarized and interpreted before a nurse can act on them or a health educator can use them to plan lessons. This paper traces that informatics chain, from raw data to information to decisions, and examines what the program must build to use the data well and what the data cannot tell.

The Data Flow

Each sensor records the date and time of every actuation and stores the records until it connects by Bluetooth to the student's phone. The app uploads the records to the vendor's cloud server, where they are linked to the student's account and to the inhaler type, controller or rescue. The vendor's dashboard displays each enrolled student's data for authorized users, in this case the school nurses and the program's care coordinator, and can export summary files. Four points in this chain can fail: the sensor can lose charge or fall off, the phone can be off or out of range, the upload can be delayed when the phone has no data connection and the account can be linked to the wrong inhaler. Understanding the flow tells the program where to look when data seem missing or strange.

From Raw Data to Information

Raw time stamps become useful when they are converted into measures. For controller inhalers, the key measure is adherence: the percentage of prescribed doses recorded over a week or month. In a randomized trial among school-aged children, electronic monitoring with reminders raised median adherence to inhaled corticosteroids from 30% to 84%, showing both how low adherence can be and how measurable it is (Chan et al., 2015). For rescue inhalers, useful measures include the number of days per week with any use, the number of nighttime uses and the timing of use relative to school hours or physical education. These measures matter clinically. National asthma guidelines treat rescue inhaler use for symptom relief on more than two days a week as a sign that asthma is not well controlled (National Asthma Education and Prevention Program, 2007). Converting time stamps into these measures turns a list of puffs into a picture of each student's control.

Decision Rules for Individual Students

Information becomes action through decision rules agreed in advance. The pilot will use three. If a student's rescue inhaler use exceeds two days in a week, or includes any nighttime use two weeks in a row, the dashboard will flag the student, and the school nurse will contact the student within two school days to review symptoms, check inhaler technique and, with consent, notify the family and the student's clinician. If controller adherence falls below 50% for two consecutive weeks, the nurse will meet the student to explore barriers, such as forgetting, side effects or a lost inhaler. If no data arrive for seven days, the care coordinator will check whether the sensor or phone has a problem before assuming nonuse. These rules connect the data to guideline-based care while keeping the workload predictable for nurses.

Checking Data Quality

Decisions are only as good as the data. The program will apply simple checks drawn from a common data quality framework that asks whether values conform to expected formats, whether expected values are present and whether they are plausible (Kahn et al., 2016). Conformance checks will confirm that each sensor is linked to the right student and inhaler type. Completeness checks will flag gaps in syncing, which are likely during weekends or when phones are off. Plausibility checks will look for impossible patterns, such as twenty actuations in one minute, which may reflect a student playing with the inhaler or a sensor malfunction rather than twenty doses. Recording why data are missing or implausible, rather than silently excluding them, helps the program understand how the technology performs in real life.

From Individual Data to Program Decisions

Aggregated and de-identified, the data can also guide health education. Patterns across students may show rescue use rising on days with physical education, on days with high pollen or wildfire smoke, or in particular schools. Such patterns suggest specific responses: a lesson with coaches and physical education teachers on pre-exercise inhaler use and warm-ups, air quality alerts sent to students with asthma on high-risk days or a closer look at conditions in a school building. Adherence patterns may show that controller use drops on weekends or during school breaks, pointing to reminders and family education targeted to those times. Merchant et al. (2016) showed how population-level monitoring of rescue use, combined with outreach, reduced rescue inhaler use compared with usual care, illustrating how data from many individuals can support a program's management of a whole population.

What the Data Cannot Tell

The data have important limits. A recorded actuation shows that the inhaler was pressed, not that the student inhaled the medicine correctly, so technique must still be checked in person. Missing data may mean nonuse, a dead battery or a phone left at home. Rescue use reflects symptoms but also a student's habits and fears; some teenagers use a rescue inhaler before every game out of anxiety, while others tolerate symptoms without using it. The data also cover only the students who enroll, likely those more engaged or better resourced, which limits conclusions about the whole population. Finally, correlation between rescue use and conditions like physical education days suggests a trigger but does not prove it. The program will therefore treat sensor data as one source alongside symptom questionnaires, nurse assessments and conversations with students.

Protecting the Data in Use

Every step of the chain also handles sensitive health information about minors, so the informatics design must include protections from the start. Only the school nurses and the care coordinator will have dashboard accounts, each with its own login and two-step verification, and accounts will be closed promptly when staff leave. Nurses will see only the students at their own schools. Monthly program reports will use de-identified, aggregated figures, and small groups will be combined so that individual students cannot be recognized. Exports will be stored on the county's secure network rather than on personal devices. Students and parents will be told in plain language what the sensors record, who can see it and how to stop sharing. These measures are introduced here because they shape the design of the data flow; Module 4 will examine the legal and compliance requirements behind them in detail.

Building the Program's Informatics Capacity

To use the data, the program needs more than the vendor's dashboard. It needs written decision rules, as described above, built into the dashboard's alerts. It needs a care coordinator with time each morning to review flags and route them to nurses. It needs a monthly de-identified report summarizing adherence, rescue use and patterns by school, prepared by the program's data analyst. It needs training for nurses on interpreting the measures and on the data's limits. And it needs a plan for linking with clinicians, whose electronic health records may be able to receive summaries if the vendor supports standard data exchange. These elements turn a device purchase into an informatics system that supports care and education.

Conclusion

Smart inhaler sensors produce data that can reveal what students actually do with their inhalers, but only an intentional informatics process turns those data into better care and teaching. The program must understand the data flow, convert time stamps into guideline-based measures, act through agreed decision rules, check data quality, aggregate patterns to guide health education and respect the data's limits. Module 3 will examine the digital access and health literacy barriers that may keep some students from benefiting.

References

Chan, A. H. Y., Stewart, A. W., Harrison, J., Camargo, C. A., Jr., Black, P. N., & Mitchell, E. A. (2015). The effect of an electronic monitoring device with audiovisual reminder function on adherence to inhaled corticosteroids and school attendance in children with asthma: A randomised controlled trial. The Lancet Respiratory Medicine, 3(3), 210-219. https://doi.org/10.1016/S2213-2600(15)00008-9

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

Merchant, R. K., Inamdar, R., & Quade, R. C. (2016). Effectiveness of population health management using the Propeller Health asthma platform: A randomized clinical trial. The Journal of Allergy and Clinical Immunology: In Practice, 4(3), 455-463. https://doi.org/10.1016/j.jaip.2015.11.022

National Asthma Education and Prevention Program. (2007). Expert panel report 3: Guidelines for the diagnosis and management of asthma (NIH Publication No. 07-4051). National Heart, Lung, and Blood Institute.