Healthcare is a sector that struggles with inefficiencies and enormous unnecessary expenditure, can data science and technology solve the issues?
The healthcare industry is often under fire in terms of its performance and is frequently victim to widespread cost cutting. Despite this negativity encompassing the traditional healthcare environment, there are signs of progress with the introduction of digital technologies that can use data to open up new opportunities.
Through Big Data, a better, more informed and reduced cost healthcare system can be implemented. Many tech companies are taking the leap into healthcare; a record sum of $3.5bn was invested in 188 digital health companies in the 1st half of 2017 across the US, and Amazon have teamed up with other influential establishments to create a not-for-profit healthcare group in an attempt to reduce healthcare costs through proven systems.
There are enormous amounts of data available to healthcare providers, but the unstructured nature of it complicates decision making. The introduction of data science and technologies make the data easier to analyse and act upon, proving their value to the future of the industry.
The popularity of wearable technology has meant that vast volumes of data are now easily accessible. This allows for common conditions to be detected and tracked by collecting and analysing patterns in this data. Eventually, this can develop into a ‘preventive medicine program’ that uses collected data as a prompt for individuals to change their lifestyle.
One case where health data from wearables and smartphones is being used to the benefit of the public is the remote patient-monitoring system, eCare 21. By collecting health data of senior citizens from various devices, eCare 21 eliminates geographical constraints and can deliver remote consultations (similar to Push Doctor). Partnering with Big Data means they will be able to glean more useful insights and use the collected data to inform clinical decisions.
Data is, and has been, abundantly available to healthcare providers that could improve diagnosis. This said, diagnostic failure rates are high and errors can cause deaths. Using deep learning, Enlitic employs data science to read imaging data from X-rays and CT scans, analysing the data to give accurate results for diagnosis. Another side of this is using machine learning algorithms to extract and analyse samples, meaning accurate drugs can be developed on the back of the results.
Google AI engineers have claimed that machine-learning software is better at predicting outcomes than any existing software. Deaths can be predicted earlier than other methods which, in some instances, might allow for doctors to administer lifesaving procedures.
Enabled by technology data, personalised treatment and informed care can be delivered to patients by understanding each specific symptom of the disease, individual condition of the patient, their medical history and even their genetic information.
This could see the end of ‘one size fits all’ treatments that plague the sector, reducing death rates and leading to predictable medical outcomes. In a similar sense, Medaware checks prescriptions against similar cases in the database and informs the doctor of what is best to use for the specific scenario, saving money and reducing risk of lethal outcomes through prescription errors.
Applying appropriate technology will help make better sense of data as it will suggest recommendations and highlight actionable insights. This could include clinic staff scheduling, reducing wait times, managing supplies and accounting, as well as building efficient action programs for the outbreak of epidemics.
Hospital readmissions can be reduced by digitisation and technical transformation by using algorithms to identify the patients who are most at risk, coordinating the necessary care as a result. This is an area that enormous cost savings can be made.
We are on the verge of the healthcare industry as we know it being totally re-shaped. Inefficiencies and wastefulness are rife in the sector, and whilst everyone recognises them as widespread, only now are steps being taken to rectify the issues. Money can be saved and processes can be improved - data science is enabling this revolution.