Predictive health promises a bright new future – from battling chronic conditions, to spotting potentially fatal diseases faster.

Yet currently, many hospitals do not have the means to access this data and use it for analysis. What can they do to reach the full potential of their data?

We explore how hospitals can overcome hurdles to transform patient care.

The problem

Complete and accurate records are essential to diagnose and treat patients. But manual updates take time, and risk accuracy.

Medical insights are often “trapped” in free-form medical text, including hospital admission notes and a patient’s medical history. And tech systems risk not interacting with one another, meaning that valuable data is not shared.

This can become more complicated when doctors use different terms for the same condition. For instance, “atrial fibrillation” may sometimes be recorded as “AF” instead.

The solution

Tech can make extracting key insights from medical data easier. Cerner Corporation, an American healthtech supplier, is developing a digital voice scribe to make transcribing doctor-patient interactions easier. After completing the transcript, the scribe inputs key findings into an electronic health records system.

Cerner used Amazon Transcribe Medical to build the scribe. The service uses machine learning to accurately identify medical terms such as diseases and medicine names. Beyond capturing in-person conversations, it can also subtitle telemedicine consultations.

Hospitals must also protect their patients’ privacy. They must capture consent for data sharing, ensure anonymisation where appropriate, and guard their systems against security risks.

The tool can redact sensitive information, such as identification numbers and addresses, in the transcripts. Hospitals can then easily review and share the transcripts to help improve patient care.

Hospitals can also use AWS’s natural language processing service, Amazon Comprehend Medical, to quickly sieve out crucial medical information, such as the dosage and frequency of medication. It can process multiple sources including doctors’ notes, clinical trial reports, and patient health records, and even correct abbreviations and typos.

For instance, doctors could use Amazon Comprehend Medical to examine symptoms to identify patients at risk of multiple sclerosis. The tool is also helpful for identifying suitable candidates for clinical trials. All this data is anonymised, and is not stored by the service.

Enter the cloud

AWS cloud services have given healthcare providers a more comprehensive view of patients. It helped AWS Healthcare Competency Partners to store, transmit and analyse clinical data along the patient journey. Organisations can easily customise new interoperability tools to fit their needs.

Additionally, the cloud’s scalable storage and computing power is helpful for carrying out big data analytics. This is important for making better decisions, and usually requires temporary but very high loads of storage and compute. The cloud is also a more cost-effective way of managing an organisation’s IT infrastructure.

There is a great deal of value hidden away in healthcare data, and this also means there is vast potential for growth. With the right tech tools, healthcare organisations will be able to uncover precious information to help them improve patient care, conduct better research and transform the future of health.