Device Data, Teleaudiology and Privacy (KVKK)

Gains:

• Understanding the privacy risks and KVKK obligations of hearing aid and scanning device data (data logging, application data)
• Ability to design the use of artificial intelligence in a safe and anonymous manner in teleaudiology (remote fitting and monitoring) flow
• Ability to apply limits and anonymization steps of sending patient data to cloud-based artificial intelligence tools

Modern audiology no longer works solely with data generated in the clinic. Hearing aids and mobile apps are constantly collecting data; patients are monitored remotely; scanning devices are connected to cloud systems. While this offers powerful opportunities, it also creates serious privacy responsibilities. In this unit, we will cover the privacy risks of hearing aid and screening data, the safe use of artificial intelligence in teleaudiology (remote application and monitoring), and the limits of sending patient data to cloud-based tools. Main principle: Patient and device data are special quality health data; It requires the highest protection within the scope of KVKK. Before sending data to artificial intelligence, it is anonymized, a secure tool is selected and only necessary data is shared.

What device data does it collect?

Modern hearing aids and applications perform data logging: how many hours a day the device is used, in which environments (quiet, noisy, music) it works, which programs/sound levels the patient prefers can be recorded. Mobile apps can add location, usage habits, and sometimes audio media data to this. This data is valuable for tracking and tracking — but it also contains sensitive information about a person's living arrangements.

Within the scope of KVKK (Personal Data Protection Law), health data is special quality data. This means that its processing requires stricter conditions, explicit consent and high security measures. Hearing aid data is also included in this scope.

Caution: "Appearing to be anonymous" data is not always anonymous. When a rare hearing loss profile is combined with a certain age and location, the person can be recognized again. Anonymization is not just about deleting the name, but about evaluating every clue that might lead back to identity.

Limits of sending data to cloud AI tools

Everything you type into a general-purpose, public-facing AI tool goes to that service's servers, and you have limited control over what happens. Because:

• Real name, T.C. Direct identifiers such as ID number, address, telephone, device serial number are never entered.
• Rare/unique combinations of details that could lead back to identity are avoided.
• If possible, corporate, secure tools with clear data processing conditions are preferred.
• Only the minimum data necessary to perform the task is shared (data minimization).

Data type

Can he enter a public vehicle?

safe alternative

Name, T.R., contact

never

anonymous code

Device serial number

never

Device type (general)

Raw audiogram + ID

no

Anonymous, anonymous thresholds

Anonymous clinical summary

Conditional (safe vehicle)

No ID, minimal data

Data logging + identity

no

Anonymous summary trend

Safe artificial intelligence in teleaudiology

Teleaudiology is a practice in which the patient is evaluated remotely from outside the clinic (from his home), and his device is adjusted or monitored remotely. Artificial intelligence is here; It can help with tasks such as summarizing remote usage data, drafting responses to patient messages, or creating follow-up reminders. But a setting recommendation based on remote usage data cannot be applied directly: the patient's actual complaint must be verified with the hearing profile and, if necessary, a new measurement, and the device setting should not be changed permanently without approval by the expert.

Step by step: privacy-secure workflow

1. Take a data inventory. Determine which part of your data reveals identity.
2. Anonymize. Extract direct identifiers, generalize rare combinations.
3. Select vehicle. If the task is sensitive, choose a secure/enterprise tool; putting sensitive data on a public tool.
4. Share minimum data. Send only enough to get the job done.
5. Verify the suggestion remotely. Confirm teleaudiology recommendations with clinical evaluation and measurement if necessary.
6. Monitoring and consent. Verify that the necessary consent for data processing has been obtained and a record is kept.

Weak prompt / Strong prompt

Weak prompt:

This is the usage data from Ayşe Demir's (TC 123...) device application, how should I change the setting: [full data]

It includes direct identity and TC (severe KVKK violation), and requests a setting decision without verifying remote data.

Powerful prompt:

Your role: teleaudiology follow-up assistant. Making a final adjustment DECISION; attribute the recommendation to expert verification.Data (anonymous): 65 years old, RIC device, ~4 hours daily use in the last 2 weeks, mostly noisy environment, patient says "I can hardly understand speech in noise".Task: list which possible adjustment aspects can be reviewed from this trend; For each aspect, write how it will be verified with the patient and/or measurement. State that permanent adjustment changes require expert approval and new measurements if necessary.

The strong prompt is anonymous, uses minimal data, and links the recommendation to validation and expert approval.

three mini cases

Case 1 — Return from violation. An audiologist is about to paste a patient's full name, TC, and full audiogram into a publicly available AI tool. Remembers the corporate privacy rule; it strips away identifying information, anonymizes data such as “age 58, right middle-advanced sensorineural,” and shares only thresholds required for interpretation. Clinical intent is met, no one is exposed.

Case 2 — Remote recommendation validation. In teleaudiology, data from a patient's app is summarized by artificial intelligence so that "noise program gain can be increased." The audiologist does not apply this directly; He or she makes a video call with the patient and clarifies the complaint, plans REM when necessary, and changes the setting only after approval. Usage data became a clue, the decision was left to the expert.

Case 3 — Risk of re-identification. An expert is about to write, "7-year-old child with hearing loss associated with a rare syndrome is the only case in County X." He realizes that this combination makes the person recognisable; omits location and rare detail, leaving only clinically relevant information. Not writing a name alone did not provide anonymity.

Copiable prompt templates

ANONYMIZATION + MINIMIZATION TEMPLATEIn the following text, (1) remove direct identifiers (name, ID, contact, serial number), (2) generalize rare combinations that can make the person recognisable (location + rare diagnosis + age), (3) delete any data unnecessary for the task. Text: [text]

DATA INVENTORY TEMPLATEIn the data set below, classify each field as "direct identifier / indirect identifier / clinically-essential / unnecessary" and indicate which should not be sent to the tool. Data: [fields]

TELEODIOLOGY RECOMMENDATION VERIFICATION TEMPLATEList possible adjustment directions from the remote use data below; For EACH aspect, write the step for verification by interviewing the patient and/or measurement. Permanent change requires expert approval, state this. Data (anonymous): [usage data]

CONSENT/MONITORING CONTROL TEMPLATEList the items that need to be checked in terms of KVKK in a teleaudiology/data processing process (explicit consent, data minimization, secure tool, storage period, access record). Give it as a checklist.

Common mistakes

• Entering identification data into the vehicle. Violating KVKK with name, TR ID and serial number.
• Thinking "I deleted the name and it became anonymous". Ignoring the risk of re-recognition of rare combinations.
• Putting sensitive data on a publicly available tool. Driving general rather than safe driving when the task is sensitive.
• Sharing too much data. Bypassing data minimization and sending unnecessary information.
• Applying remote suggestion directly. Making the teleaudiology adjustment recommendation permanent without expert approval and verification.

In summary

Hearing aid and screening data are special health data and KVKK requires the highest protection. Before sending data to the AI, direct identifiers are extracted, combinations at risk of being re-identified are generalized, the safe tool is selected, and only the minimum necessary data is shared. In teleaudiology, suggestions coming from a distance are valuable clues, but they do not become permanent adjustments without expert approval and verification.

Application task

Define an anonymous "dataset" (containing mixed fields such as name, ID, device serial number, age, location, rare diagnosis, audiogram thresholds, etc.). Classify each field with the “data inventory” template, then secure it so it can be sent to the tool with the “anonymization + minimization” template. Also, for an example of teleaudiology usage data, apply the “recommendation verification” template and write the verification step for each setting aspect.

checklist

• [ ] I removed the direct identifiers (name, ID, serial number).
• [ ] I have generalized the combinations that are at risk of re-identification.
• [ ] I chose the safe/appropriate tool according to the sensitivity of the task.
• [ ] I shared only the minimum required data.
• [ ] I have linked the teleaudiology recommendation to expert approval and verification.
• [ ] I have checked consent and monitoring obligations.