The use of smart data analytics and Business Intelligence is becoming the norm. The healthcare industry is not behind in this trend. 57% of healthcare organizations have implemented patient data analytics to improve patient care and outcomes. And 46% of organizations have implemented analytics of their organizational data to improve their everyday performance.
Actually, medical organizations are key users in business intelligence. They generate enormous amounts of data. And due to legal regulations, they need state of the art BI to analyze it while keeping it safe. Just to get an idea of the amount of data in the healthcare industry and its potential, let’s go over some numbers. According to the University of Iowa, Carver College of Medicine, medical data is expected to double every 73 days by 2020. The volume of health data is growing exponentially; it is expected to be 50 times larger by 2020. This exponential growth can be due to the fact that more than 16,000 hospitals collect data on patients worldwide. And 4.9 million patients use remote monitoring devices (including wearables like Fitbit). Plus, patient monitoring equipment produces an average of 1,000 readings per second.
Now let’s go over some of the benefits of using data analytics. There can be a 20% decrease in patient mortality by analyzing streaming patient data. Of organizations that analyze their data, 82% reported improved patient care, 63% reported reduced readmission rates, 62% reported improved overall health outcomes, 54% reported improved financial reporting capabilities, 50% reported improved hospital operational performance and 49% reported improved management decision-making.
Healthcare organizations are implementing data analytics to improve efficiency and patient care. Smart data analytics or BI tools can help organizations analyze the large volumes of data they generate. This data has an enormous potential to reduce operational costs, improve quality of patient care, identify patterns and even save lives. Hospitals can integrate with third-party data sources to do a benchmark of national averages. They can compare their internal metrics and national metrics to revise their processes and improve their operations.
Medical data can also be used for prevention. For example, to prevent the spread of epidemics, population movements can be tracked with mobile phone location to predict the spread of viruses like Ebola. This allows organizations to identify which regions need urgent allocation of resources and treatment centers. There are new trends appearing, such as Personalized Medicine. It means customizing medicine to a person’s unique genetics. This is done by analyzing together both a person’s genetics and information about their lifestyle. This data can be analyzed together with thousands of others and be used to predict illness patterns.
What to do with BI?
There are many things healthcare organizations can do with their data. The following is a list of 20 things that they can do. This list is not closed, meaning there are many more uses and benefits, because the tools can adapt to each organization’s needs. With a good centralized BI tool, healthcare organizations can:
- Connect to multiple data sources and analyze their data in a secure environment.
- Find hidden insights to improve overall performance.
- Identify patterns of cost and profitability.
- Understand what are the challenges by department and/or specialty.
- Provide dashboards with beautiful visualizations to staff.
- Get automatic insights that can be easily turned into action to improve the quality of patient care.
- Do accurate allocation of resources.
- Measure the impact of new programs and initiatives.
- Monitor KPIs like patient wait times, bed rotation, nurse rotation, patient volumes by time of the day or day of the week, etc.
- Compare data to other hospitals per region using Geo-analysis.
- Analyze and compare costs of medical procedures by hospital, by region, etc.
- Improve doctor and nurse productivity by generating automatic reports and notifications, allowing doctors to focus on patient care rather than paperwork.
- Prioritize care for those patients who need it most.
- Achieve data-driven operations by providing personalized dashboards and role-based reports to users, from nurses to executives, giving them the information they need for making the right decisions, at the right time and in the right context.
- Manage cases efficiently by tracking care coordination interventions, care transition assessments, and readmission risk.
- Conduct hazard vulnerability analysis.
- Analyze costs of different medical conditions. It is easy to analyze data of hospitals by locations and information on average cost to care for a patient. Then using Geo-analysis capabilities, we can identify the locations to prioritize cost reductions.
- Identify patients with greater readmission rates. By analyzing data such as gender, age, average cost for care and patient history, we can group the characteristics of patients most likely to be readmitted.
- Drill down patient demographic data to discover hidden insights. For example: male patients between the ages of 25-45 have spikes in cost in a certain period of time in a certain location. These insights can then be analyzed further to obtain more insights.
- Cross analysis of patient readmission and different medical conditions. By analyzing total patient cost, readmission rates and diagnosis, we can find insights and trends in the most susceptible patient groups and the most and least costly conditions to treat over time.
At the top of the list we mentioned that these things can be achieved using a centralized BI tool. This needs to be explained in more depth, because the right BI model can ensure a BI project’s success in healthcare.
Centralized BI in Healthcare
The correct implementation of a BI project is a big challenge for any company. Healthcare organizations face an even bigger challenge when implementing BI because of the nature of the data they handle. They generate, collect, and analyze sensitive data, like patient records, patient financial information, etc. A lot of this data is regulated by strict privacy rules. An example of this is the Health Insurance Portability and Accountability Act (HIPAA), which calls for extra security and a special administration of the data.
Only certain individuals are allowed to look at private patient records. Healthcare organizations have tried different models of organizing their BI teams. Some have special BI teams under the supervision of a Chief Medical Officer, a Chief Financial Officer and a Chief Operating Officer. This model ends up creating silos. Each team works with a different person, each has their own data and work, edit, comment, and analyze only on their desktop. The data is siloed in archives controlled by different administrative departments, doctors, clinics, and hospitals. At the end of the day, for the fear of sharing data that is restricted, they are also not sharing data that could benefit the whole organization.
The insights they find stay only in one team. Then it is impossible to try to merge the data. And in these failed attempts to merge data, they realize that there are many versions of the same files… many versions of the “truth”. It is a data chaos. And we know that no business can allow data chaos, but in a hospital this might mean the endangering of lives. What if 10 people save the same patient information differently? Which one is the real one?
It seems like having one version of the truth and data accuracy plus data privacy is an impossible task. But it’s not. Information needs to be updated for everyone in one same web solution, with IT overseeing who has permissions to access it and who doesn’t. The best solution is for BI users to be able to analyze the data in a centralized environment. Meaning they can share insights and resources between departments, but IT will govern over the data (making sure to protect it and stand by HIPAA and other regulations). The different users can benefit from the data, but IT will decide who gets to see what and what is public and what is confidential. Using a model like this ensures data privacy and one version of the truth. IT will give the users the support and resources they need.
Using centralized BI allows organizations to achieve consistency in data. Everyone in the organization will be looking at the same numbers, results, codes, practices, etc. There will be consistency of data in physician masters, patient indexes and records, diagnosis codes, procedure codes, etc.
Necto is the only centralized, secure and state of the art BI solution that can enable healthcare organizations to achieve BI success while maintaining one version of the truth in their data.
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