Learn about healthcare analytics and how it can
make your organization more efficient.
The healthcare industry is large and complex. This complexity is compounded by strict privacy regulations, rapidly evolving viruses and diseases, and varying data standards across all the different organizations involved.
Because of this, all sorts of healthcare organizations are increasingly relying on healthcare analytics to address this complexity to create a more efficient and fast-responding healthcare system.
In this article, we’ll explore how healthcare analytics works and look at some examples of how it’s being applied to various parts of the industry.
Healthcare analytics is used in the healthcare industry to understand past trends, optimize operations, and make predictions about the future. It can be utilized at both the micro level with individual patient outcomes, as well as at the macro level with broader industry-wide trends.
Since the healthcare industry is broad and involves many different types of organizations, there are numerous applications of healthcare analytics. For example, pharmaceutical companies can run bioinformatic models to find novel new drugs, primary care providers can cross-reference patient electronic health records (EHRs) with hospital databases to perform more accurate diagnoses, and insurance providers can use various third- and first-party datasets to perform their actuarial analysis more efficiently.
Whatever the use or application, properly leveraged analytics data helps organizations make faster and more accurate decisions that ultimately improve the outcomes for patients.
As we learned from the COVID-19 pandemic, it’s essential that the healthcare industry is able to quickly respond to new threats — be it a new virus variant or a supply chain bottleneck. Yet, traditional methods of healthcare are no longer sufficient to meet these fast-moving threats.
Healthcare analytics allows all different types of organizations to leverage the vast amounts of data produced from drug trials, hospitals, electronic health records, supply chains, and much more to better understand both macro and micro trends to get ahead of issues and ideally even predict their path.
For example, throughout the pandemic, the healthcare industry has been constantly dealing with new variants of the virus, while simultaneously addressing supply shortages that limited its ability to fully respond. The good news is that due to new sequencing, data collection, and statistical modeling techniques, we’re actually able to track the spread and mutations of the viruses with pretty high levels of accuracy. This allows doctors and policy makers to prepare for new spikes in cases, while providing pharmaceutical companies valuable data to begin preparing new vaccines and treatments.
Even outside of a pandemic, there healthcare analytics has many benefits:
There is a wealth of data associated with each and every patient. Their electronic health records can include everything from physician examination notes to lab results to pharmacy records. This data can be invaluable to more accurately diagnosing symptoms, prescribing medicines, and ensuring continuity of care.
Hospitals can cross-reference patient electronic health records with nation-wide statistics to automatically provide the doctor with likely causes of ailments based on symptoms and pre-existing conditions. This can help guide doctors to treat patients more accurately with less margin of error.
Like other businesses, healthcare organizations can leverage data as a competitive advantage. Whether this means insurance providers able to offer better premiums based on better actuarial models or pharmaceutical companies able to bring a novel drug to market faster, data analytics is key to making smarter, faster decisions and enables businesses to gain market share over their competition.
In addition to growing market share, data analytics can allow healthcare organizations to identify other key insights about their businesses that can drive actionable change. For example, you can analyze data to understand real-time bottlenecks in a production process to streamline operations, reduce unnecessary overhead, and maximize margins.
Fast and accurate reporting of macro health data is essential to reducing the spread of viruses and other diseases. Data analytics allows policy makers, pharmaceutical companies, and hospitals alike to understand how these population-wide conditions are changing and allow for rapid response to contain and ultimately prevent major health issues.
While it’s pretty clear how valuable healthcare analytics can be to the industry, those implementing the systems to enable it will need to take a few important strategies into consideration:
The importance of using accurate data in a healthcare setting cannot be overstated. However, processes like manual data entry can cause errors and inconsistencies. Therefore, it’s important to have processes in place to identify and resolve low-level data issues on an ongoing basis so decision makers can be confident in their analyses.
Oftentimes, data analysts will ignore or prune outlier events when building their statistical models. However, the needle in the haystack can be critical in healthcare as rare events do happen and those patients must be properly cared for. AI can also be leveraged to help shape what questions to ask instead of just answering the known questions.
Healthcare data contains extremely sensitive information and is heavily regulated by laws such as HIPAA. Any analytics system that is built on top of data such as electronic health records must be built with a security-first approach to ensure that data is properly secured and access controlled so that only those that are permitted to access the data do so.
Even if we eliminate human biases from healthcare, it’s actually possible to have statistical biases that cause inaccurate analysis. For example, using data that is primarily about patients of a certain race, gender, or geography may trigger false positives and/or false negatives when used for a patient of the underrepresented group. Therefore, it’s important to understand the makeup of datasets and what types of inherent assumptions may be present that could introduce unexpected bias.
Given the size and complexity of the global healthcare industry, there are endless opportunities to optimize all different systems and processes.
For example, a new and rapidly evolving application of healthcare analytics is in developing novel new drugs. In addition to automating and analyzing a lot of the drug trial process, pharmaceutical companies can even build bioinformatic models to predict types of drugs that could have efficacy in fighting a disease even before the potential drug is produced.
Being able to stay ahead of new viruses and diseases is becoming essential to keeping our growing global population safe. Having these types of data models allows researchers to quickly experiment with endless combinations of vaccine and drug possibilities to maximize the chance of efficacy and safety before moving on to physical trials.
Healthcare analytics is an essential tool for making healthcare operations more efficient and keeping people safe. To realize its benefits, you must be able to integrate disparate data sources, secure and control access to information to comply with regulations, and model and visualize your data at scale.
ThoughtSpot’s Modern Analytics Cloud provides all the features necessary for healthcare organizations to manage these complex operations and begin utilizing their data. Try a free trial of ThoughtSpot to see how you can start creating personalized, actionable insights from data at your healthcare organization today.
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