Big data has transformed many industries by performing analysis and conducting forecasts that were previously deemed impossible. One such area where big data has brought about a considerable change is in the healthcare industry. The use of big data in healthcare has the potential to reduce the treatment costs, improve operational efficiency, improve patient outcomes, and improve the quality of life. Today, healthcare professionals have access to large volumes of data like EHR, which help them devise effective strategies. The rising cost of healthcare services is a major factor behind the use of big data in healthcare. For instance, healthcare expenses represent about 17.6% of the nation’s GDP in the US. Smart, data-driven thinking can significantly reduce such expenses without compromising outcomes. Here are some of the best examples of the use of big data in healthcare.
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Big Data Assists in Operational Management in Hospitals
One of the most significant problems faced by healthcare facilities is to allocate healthcare personnel in a given shift optimally. Too few and the quality of patient care deteriorates, too many and it unnecessarily increases the operational cost. Four hospitals in Paris, which are part of the Assistance Publique-Hôpitaux de Paris, have been using big data to forecast the patient footfalls at a given time and date. A key data point to perform such analysis is obtained from a historical hospital admissions record, which goes back as far as ten years. The data scientists then performed a time series analysis to see relevant patterns in the admissions rate. Feeding this data into machine learning programs can fetch the best algorithms to predict future admission trends.
Electronic Health Records (EHR)
EHR is one of the most prominent examples of the use of big data in healthcare. It is a master repository of patient data that includes their demographic information, medical history, test results, and allergies. These records are then shared among healthcare practitioners in both the public and private sectors to provide better care. Healthcare practitioners can add new data into the records as they give care to the patients without the fear of data duplication. The advancements in EHR have progressed to such levels that it can also trigger warnings to track prescriptions and send reminders when a patient gets a new lab test. The US has been the model country in implementing the EHR with a 94% adoption rate, while other countries are still struggling to implement them fully.
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Prevention of Medicine Abuse
Medicine abuse can be fatal and lead to consequences as bad as accidental deaths. In the US alone, opioid abuse has caused more accidental deaths than road accidents, overtaking it as the most common cause of accidental death. The Obama administration spent close to $1.1 billion to tackle this problem. However, scientists at Blue Cross Sheild have started working towards the problem, which would cost a fraction of that $1.1 billion. In partnership with big data experts at Fuzzy Logix, they start out by analyzing years of insurance and pharmacy data to identify 742 risk factors that predict whether someone is at risk for abusing opioids. As a result, it helped the concerned authority to prevent drug abuse cases by identifying the people who were at a higher risk.
A Cure for Cancer
The Obama administration came up with the Cancer Moonshot program, which had panels that made recommendations towards finding an effective cure for cancer. One such suggestion made from the panel included the case of big data use. Medical researchers are studying a variety of data to find trends and treatments and recovery rates of cancer patients. The researchers can spot the treatment plan with the highest success rate and also point out the best treatment plan for each patient. Additionally, by examining tumor samples and patient treatment records, data scientists can figure out how specific mutations and cancer proteins interact with a variety of treatment plans provided to the patient. One of the most promising use cases of big data in the healthcare industry has to be genetically sequencing cancer tissue samples from cancer trial patients to find a viable solution.