How To Get Healthcare Analytics Right
The use of data Analytics is arguably the most significant development in healthcare in the last ten years. So say many industry pundits, including Sanjeev Agrawal, CIO of LeanTaaS. Insurance, Medicare and other agencies increasingly want to know more than if a service was provided; they want to know the outcome of the patient treatment. In most cases, that involves analytics. However, making the most of this dynamic tool depends on using data and the conclusions you can draw from it in the best possible ways and communicating them effectively. What does that mean for your organization or your medical practice? Consider the following:
Data Analytics in Healthcare: Best Practices
1. Encourage collaboration. One of the worst things you can do is to have each department analyzing the same data at the same time. What a waste of valuable talent and human resources. Instead of this duplication, put in place systems that makes it easy for departments to work together and share the outcome of their analyses. Involving the end user early in the process is also important to making sure that the analysis is answering the question the end user has posed. "Collaboration is a cornerstone to helping drive outcomes," according to Bradley Hunter, a research director at KLAS Research. Ideally, both administrative and clinical personnel should participate in any proposed analytics.
2. Make use of natural language in EHR. Using less clinical language when working with electronic health records (EHRs) and other data input makes it easier for non-medical staff and artificial intelligence to understand and work with these records and this data. Starting this practice now can give your organization a big step forward as AI becomes more prevalent in healthcare data analysis.
3. Let a business case drive your analysis. Spending time on data analytics may be interesting, but in and of itself, such analytics don't often further your business. For best results from data analytics, use the data to answer and support a business case, such as how to shorten patient lengths of stay, how to cut costs without sacrificing the quality of patient care, how to provide better patient care or how to best fill in the gaps in patient care.
4. Start with those applications where time and resources can be saved. The best starting point for data analytics in any healthcare organization is to look for ways to immediately free up either time or money or both. These applications can be either on the administrative or the clinical side of your operation lowering professional liability coverage costs for health professional across the board.
5. Hone your data visualization communication skills. Data analysts need to be great communicators. Even the best and brightest analysts won't be effective if they can't translate their results into language that the end users will understand. Tableau Software calls increased communication skills among data analysts one of the trends to watch for in healthcare analytics in 2019. Data visualization, also called data storytelling, is one of the best ways to overcome communication issues between technical and administrative staff. Simply put, data visualization is the art and science of using visual elements like charts, graphs and maps to help explain the steps taken to arrive at the analytics team's conclusion.
Data analytics in healthcare doesn't have to be intimidating. To get the most out of this useful and increasingly essential tool, encourage interdepartmental sharing of information, use natural not technical language in your EHRs and other data input, utilize analytics to help solve a business concern, become experts at using and learning via data visualization and begin any analytics project by looking for ways to free up time and money.