Big data refers to large sets of complex, diverse data that can be analyzed to uncover patterns, trends, and insights. In the healthcare industry, big data can be collected from a variety of sources, such as electronic health records, patient monitoring devices, and insurance claims.
One way big data is used in hospitals is through electronic health records (EHRs). EHRs are digital versions of paper patient records, and they allow healthcare providers to easily access and share patient information. By analyzing data from EHRs, hospitals can identify patterns and trends in patient health, such as common illnesses or risk factors for certain diseases. This can help hospitals improve patient care and make more informed decisions about treatment options.
Another way big data is used in hospitals is through predictive analytics. Predictive analytics uses data and statistical algorithms to identify the likelihood of future outcomes. In healthcare, this can be used to predict patient outcomes, such as the likelihood of a patient developing a certain condition or responding well to a certain treatment. Hospitals can use this information to improve patient outcomes and reduce healthcare costs.
Big data can also be used in hospitals to improve operational efficiency. For example, hospitals can use big data to optimize staffing levels, reduce wait times, and improve patient flow. Additionally, big data can be used to track and monitor the spread of infectious diseases. This can help hospitals respond more quickly and effectively to outbreaks.
In summary, big data is used in hospitals to improve patient care, reduce costs, and optimize operations. Data is collected from various sources such as electronic health records, patient monitoring devices and insurance claims, and analyzed to identify patterns, trends, and insights that can inform clinical decision making, improve patient outcomes, and support operational efficiencies.
Multiple examples of using data and predictive analytics for improving healthcare operational efficiency, optimizing staffing levels, reducing wait times, and improving patient flow are presented in the book (see attached),
as well as in the review "Management science for healthcare applications." (see attached).
See also an introductory course offered for National Health System (NHS), the UK, in 2022 and 2023.
Velibor Božić
Dear Professor,
Thank you for the interesting topic of discussion.
While pursuing a research, you will develop some research questions. Then while collecting data from hospitals for your research, try to find out which hospital has business objectives that match with your research questions. Then Collect the data from those hospitals. As far as my understanding goes, no hospital will be able to produce data that will suit your research. After collection of the data, you have to give the share and organization to the data so that it is able to meet your research purpose.
Best regards,
Anamitra.
Hi - this is a big subject in healthcare and there are already advaced solutions on the market.
In general though, the data is "closed". That is, held inside a health record system (EHR) or a Population Health Management System. There are some experiemntal health data lakes in deelopment like NHS Secure Data Environments https://digital.nhs.uk/services/secure-data-environment-service but these are relatively new. Of cousre patient information is confidential (and subject to the privacy/consent laws of the countries where data managed - Google got into hot water oer this in an NHS London healthcare datalake pilot implementation).
If the data is only for statistical use, it is usually anonymised (or pseudomimised) for analytics in these big data environements.
Ultimately, this patient centric data can lead to discoveries in personalised medicine - but this requires patient identifyable information to be part of the datalake.
With respect to prductivity and efficiency (business analytic). The information is more distributed in departmental or practice based systems (for example, you can get stats for radiology production/throughput from Radiology Information Systems, and only limited infor about this from EHRs) Here theree is a good argument for a data lake, but the difficulty is tagging the events in a meaniful way such that differenc departments or practices can have their data compared.
While there are international standards for clinical healthcare information there are none for this business analytics information.
Basically what Ii am sayin is the clinical information is easier to normalised than the business practice information.
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