Tag Archives: medical records

How ‘Digital mask’ protects patients’ privacy [Details]

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Scientists have created a ‘digital mask’ that will allow facial images to be stored in medical records while preventing potentially sensitive personal biometric information from being extracted and shared.

In research published today in Nature Medicine, a team led by scientists from the University of Cambridge and Sun Yat-sen University in Guangzhou, China, used three-dimensional (3D) reconstruction and deep learning algorithms to erase identifiable features from facial images while retaining disease-relevant features needed for diagnosis.

Facial images can be useful for identifying signs of disease. For example, features such as deep forehead wrinkles and wrinkles around the eyes are significantly associated with coronary heart disease, while abnormal changes in eye movement can indicate poor visual function and visual cognitive developmental problems. However, facial images also inevitably record other biometric information about the patient, including their race, sex, age and mood.

Digital masks

Graphic showing digital masking process/Photo:Professor Haotian Lin’s research group

With the increasing digitalisation of medical records comes the risk of data breaches. While most patient data can be anonymised, facial data is more difficult to anonymise while retaining essential information. Common methods, including blurring and cropping identifiable areas, may lose important disease-relevant information, yet even so cannot fully evade face recognition systems.

Due to privacy concerns, people often hesitate to share their medical data for public medical research or electronic health records, hindering the development of digital medical care.

Professor Haotian Lin from Sun Yat-sen University said: “During the COVID-19 pandemic, we had to turn to consultations over the phone or by video link rather than in person. Remote healthcare for eye diseases requires patients to share a large amount of digital facial information. Patients want to know that their potentially sensitive information is secure and that their privacy is protected.”

Professor Lin and colleagues developed a ‘digital mask’, which inputs an original video of a patient’s face and outputs a video based on the use of a deep learning algorithm and 3D reconstruction, while discarding as much of the patient’s personal biometric information as possible – and from which it was not possible to identify the individual.

Deep learning extracts features from different facial parts, while 3D reconstruction automatically digitises the shapes and movement of 3D faces, eyelids, and eyeballs based on the extracted facial features. Converting the digital mask videos back to the original videos is extremely difficult because most of the necessary information is no longer retained in the mask.

Next, the researchers tested how useful the masks were in clinical practice and found that diagnosis using the digital masks was consistent with that carried out using the original videos. This suggests that the reconstruction was precise enough for use in clinical practice.

Compared to the traditional method used to ‘de-identify’ patients – cropping the image – the risk of being identified was significantly lower in the digitally-masked patients. The researchers tested this by showing 12 ophthalmologists digitally-masked or cropped images and asking them to identify the original from five other images. They correctly identified the original from the digitally-masked image in just over a quarter (27%) of cases; for the cropped figure, they were able to do so in the overwhelming majority of cases (91%). This is likely to be an over-estimation, however: in real situations, one would likely have to identify the original image from a much larger set.

The team surveyed randomly selected patients attending clinics to test their attitudes towards digital masks. Over 80% of patients believed the digital mask would alleviate their privacy concerns and they expressed an increased willingness to share their personal information if such a measure was implemented.

Doctor/IANS

Finally, the team confirmed that the digital masks can also evade artificial intelligence-powered facial recognition algorithms.

Professor Patrick Yu-Wai-Man from the University of Cambridge said: “Digital masking offers a pragmatic approach to safeguarding patient privacy while still allowing the information to be useful to clinicians. At the moment, the only options available are crude, but our digital mask is a much more sophisticated tool for anonymising facial images.

“This could make telemedicine – phone and video consultations – much more feasible, making healthcare delivery more efficient. If telemedicine is to be widely adopted, then we need to overcome the barriers and concerns related to privacy protection. Our digital mask is an important step in this direction.”

Patient ID, medical records’ matching now helps during vaccination, say experts

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Experts from Regenstrief Institute, Mayo Clinic and the Pew Charitable Trusts have suggested that matching patient records from disparate sources has become crucial to stem the tide of the current coronavirus pandemic and allow for fast action for future outbreaks of highly contagious viruses.

In a peer-reviewed commentary published in npj Digital Medicine, the team of experts said rapid identification of COVID-19 infected and at-risk individuals and the success of future large-scale vaccination efforts in the United States will depend on how effectively an individual’s electronic health data is securely preserved and shared among healthcare providers, including hospitals and pharmacies, and other systems used to track the illness and immunization.

For data sharing to be effective, electronic health records (EHRs) — both those held within a single facility and those in different healthcare organizations — must correctly refer to a specific individual.

Some of the specimen queries are:

Is Billy Jones known at a different doctor’s office as William Jones and are all his health records linked? To which Maria Garcia do lab test results belong?
Which John Smith was referred to during contact tracing?

The commentary note said patient matching rates vary widely, with healthcare facilities failing to link records for the same patient as often as half the time. Authors — Shaun Grannis, vice president for data and analytics at Regenstrief Institute and Regenstrief Professor of Medical Informatics at Indiana University School of Medicine, John D. Halamka, president of Mayo Clinic Platform and Ben Moscovitch, director of the Pew Charitable Trusts’ health information technology initiative — call for stakeholders to urgently address the patient matching conundrum. Otherwise, efforts to curtail the current pandemic and future ones will be ill-advisedly delayed, they cautioned.

“The sharing of more data and use of standards — reflect near-term opportunities that government and health care organizations can implement to respond to the current pandemic and prepare for future ones. In the longer term, there may be other opportunities — such as use of biometrics, unique identifiers, or multi-factor authentication — that could further enhance patient identification and matching, including for routine care,” they said in their note.

However, those options and the associated standards that underlie their success are worthwhile to examine, but cannot be designed, deployed, and implemented in a near-term manner that could help mitigate the effects of this pandemic, said the authors.