Cloud Computing Technology in Healthcare

Smart devices and wearable sensors measure various patient vitals (e.g., body temperature, blood pressure, and glucose levels) and send the data to a cloud server for storage and analysis. If the system detects any abnormalities, both the patient and the doctor get a notification. The system may also trigger treatment delivery (e.g., insulin injection using an insulin pump), or initiate other predefined actions.

A hospital asset tracking solution uses data from RFID tags attached to hospital assets (e.g., medical devices) to track their location, optimize usage, and prevent theft. When an asset is moved, RFID readers located in the hospital rooms or halls scan the tag and send information on the asset location to the cloud server. The hospital asset tracking solution can display movable assets on an interactive hospital map or show the nearest assets upon a search request.

The data is securely transmitted from patients' and medical staff's bracelets with RFID tags to the cloud server to track their locations in real time. Such solutions can have advanced cloud-enabled analytics capabilities, such as assessing and predicting patient flow and hospital admission rates.

In AI-supported diagnostic software, a cloud-enabled AI engine processes data received from connected medical devices, EHR, patient apps, or medical staff apps. Then, it generates output based on the data and displays it in the patient app or the medical staff interface. The output can include advice on treatment routines or lifestyle changes for patients, a list of possible diagnoses for medical staff, or notifications to the patient and the doctor if the connected medical devices detect any abnormalities.

AI-supported apps can suggest personalized treatment approaches based on each patient’s unique medical history. For example, specialized cancer treatment planning apps can predict the most effective treatment methods and medications by analyzing factors such as tumor type, genetic markers, patient medical history, treatment response data, and more.

AI-supported records management software can help medical staff streamline clinical documentation and reduce the burden of administrative tasks. Such solutions may use speech recognition to enable dictation and voice commands, AI-generated auto-suggestions for data entry, data extraction from unstructured text, text-to-speech functions, and more.

Telehealth apps in primary care enable doctors to conduct online video and audio appointments, exchange information with patients in a secure and HIPAA-compliant way, issue prescriptions, and more.

Telehealth apps in acute care enable online consultations between an onsite emergency care team and a remote acute care specialist via videoconferencing and instant messaging. In this case, instead of the patient app, there is an app for the onsite emergency team, which connects to the remote acute care hub via a video streaming server hosted in the cloud.

A nurse can use a telehealth app to help patients prepare for surgery, monitor their recovery after surgery, and advise them on how to relieve symptoms. Telenursing can also be used to track adherence to treatment plans or monitor pregnancy and newborn care.

A physical therapist can use the app to help patients manage their chronic conditions and recover after surgeries and injuries in a remote setting. It is especially valuable for patients with limited mobility or those living in rural areas.