Does Big Data Worry You? Not If You Can Gain Relevance
It took about a century before the data volume in the world doubled for the first time; now it happens every 14 months. Healthcare is also generating more and more data every day, contributing to the volumes of information available. The problem with big data is that healthcare institutions often lack the right tools to bring all the information together to get the one insight that can improve the quality and efficiency of care. So, what’s the solution? Give everyone access to data that is relevant to them. By Ellen Derrico, director global market development, QlikTech
By Ellen Derrico, director global market development, QlikTech
It took about a century before the data volume in the world doubled for the first time; now it happens every 14 months. Healthcare is also generating more and more data every day, contributing to the volumes of information available.
That is hardly surprising, as health maintenance organizations (HMOs) are exposed to increasing regulatory pressure from the government, which aims to implement patient-centric processes and personalised care plans. This leads to data growth in two ways - firstly because healthcare providers, professionals, budget holders and, managers have an increasing need for guidance on current data. Secondly, because the public demands transparency and accountability, all data must be kept. Therefore, the healthcare big data mountain continues to grow and grow.
The problem with big data is that healthcare institutions often lack the right tools to bring all the information together to get the one insight that can improve the quality and efficiency of care. So, what’s the solution? Give everyone access to data that is relevant to them.
But there are challenges
That’s not to say that HMOs are standing still when it comes to managing their data and information. They have teams filled with information managers, analysts, and business intelligence specialists to answer urgent questions. And urgent issues continue to arise.
Meanwhile, carergivers are faced with rising costs, long reimbursement cycles, and long outstanding or bad debts. In addition, the industry has shifted to evidence-based medicine and pay for performance, with the next evolution to value-based purchasing tied to outcomes and more personalized and holistic patient care.
This next evolution will require the ability to integrate big data on patient demographics, disease treatments and history, and clinical trials with hospital and patient data in a meaningful way that supports and provides faster, simpler, and more accurate decision making.
Research analyst IDC has shown that the largest investment priority for 50 percent of HMOs and insurers is optimizing data analysis. Yet there are some common issues that information managers in healthcare, and in many other industries, have to deal with, which include:
- Many of the questions asked in organizations require a very fast response, especially in healthcare. How do you, as an organization, make sure those questions are answered quickly and effectively without being too reliant on IT?
- Bringing together controlled information from different databases is a time-consuming task, which means that decisions often aren’t up-to-date. How do you ensure that the report for January doesn’t just land on your desk at the end of February?
- In predefined reports it is often difficult to find outliers resulting in positive or negative outcomes. Many organizations use Excel to visualize data and display it in static graphs again. While that can help somewhat, how do you ensure that you can zoom in on a particular piece of information, like a specific patient, population, or physician, to give insightful analysis that enables the manager to know what is happening, if there is a negative deviation, or how he or she can learn from a positive outcome?
What it comes down to is that the amount of time and energy spent on data collection and processing in the reports is not sufficient. The challenge for organizations, whether in healthcare or not, is to not drown in the large amount of collected and available data, but to create maximum value by enabling anyone who works with data to retrieve the information that is relevant to them.
This is especially critical in healthcare, as decisions are made that affect lives. Having relevant data and the ability to make more objective, fact-based decisions, can improve performance and save lives. It would be more logical if the people who ultimately have to work with the data itself can access the information themselves and find the answers to their questions.
Market analyst Gartner introduced the term ‘data discovery’ for this principle two years ago. The greatest strength of data discovery is the bottom-up approach that allows everyone in the organization to gain new insights and share these with others. Gartner defines it as a form of self-service business intelligence (BI) that enables everyone within the organization to monitor, analyse and respond in real time. This is far removed from traditional BI analysis, which is static and only offers users the ability to ask predefined questions. Moreover, data discovery is much more than traditional BI – aimed at making analysis easier by visualizing and sharing insights, asking better questions, gaining additional insights, collaborating, and providing mobile access.
In the healthcare industry, data discovery offers the possibility of improving patient care by synchronizing the planning of resources with patient logistics and by helping physicians and nurses to focus on improving performance. This leads in particular to shorter waiting times and processing times, as well as shared best practices in treatment and diagnostics.
What this looks like in practice can be seen at Universitätsklinikum Tübingen Germany. The operating rooms (ORs) in this hospital have a certain capacity and, when maximum capacity has been reached, there is a waiting list. With their new data discovery approach, the German hospital was able to look differently at planning operations to better understand how they are streamlined to achieve maximum efficiency.
By analyzing relevant information, the hospital was able to perform an extra 200 to 300 operations annually, literally meaning that more lives could be saved. The data discovery software used by Tübingen (QlikView) provides clear data on how long patients have to wait for their operation, when the operation starts and ends, right up to the departure of the patient. Using the data discovery technology it is now very clear when and where there is room for improvement and where improvements can be made. By embracing big data, healthcare organizations can thrive and also, ultimately, save lives.
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