Centralized & State of The Art BI

Healthcare Industry

BI and Data Analysis in Healthcare

The use of smart data analytics andBusiness Intelligence is becoming thenorm. The healthcare industry is notbehind in this trend. 57% of healthcareorganizations have implemented patientdata analytics to improve patient careand outcomes. And 46% of organizationshave implemented analytics of theirorganizational data to improve theireveryday performance.

Actually, medical organizations are key users in business intelligence. Theygenerate enormous amounts of data. And due to legal regulations, they need stateof the art BI to analyze it while keeping it safe. Just to get an idea of the amount ofdata 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 isexpected to double every 73 days by 2020. The volume of health data is growingexponentially; it is expected to be 50 times larger by 2020. This exponential growthcan be due to the fact that more than 16,000 hospitals collect data on patientsworldwide. And 4.9 million patients use remote monitoring devices (includingwearables like Fitbit). Plus, patient monitoring equipment produces an average of1,000 readings per second.

Now let’s go over some of the benefits of using data analytics. There can be a20% decrease in patient mortality by analyzing streaming patient data. Oforganizations that analyze their data, 82% reported improved patient care, 63%reported reduced readmission rates, 62% reported improved overall healthoutcomes, 54% reported improved financial reporting capabilities, 50% reportedimproved hospital operational performance and 49% reported improvedmanagement decision-making.

Healthcare organizations areimplementing data analytics toimprove efficiency and patient care.Smart data analytics or BI tools canhelp organizations analyze the largevolumes of data they generate. Thisdata has an enormous potential toreduce operational costs, improvequality of patient care, identifypatterns and even save lives. Hospitalscan integrate with third-party datasources to do a benchmark of nationalaverages. They can compare theirinternal metrics and national metricsto revise their processes and improvetheir operations.

Data to reduce costs, improve efficiency,

and save lives.

Medical data can also be used for prevention.For example, to prevent the spread ofepidemics, population movements can betracked with mobile phone location topredict the spread of viruses like Ebola. Thisallows organizations to identify whichregions need urgent allocation of resourcesand treatment centers.

There are new trends appearing, such asPersonalized Medicine. It means customizingmedicine to a person’s unique genetics. Thisis done by analyzing together both a person’sgenetics and information about theirlifestyle. This data can be analyzed togetherwith thousands of others and be used topredict illness patterns.

There are many things healthcare organizations can do withtheir data. The following is a list of 20 things that they can do.This list is not closed, meaning there are many more uses andbenefits, because the tools can adapt to each organization’sneeds. With a good centralized BI tool, healthcare organizationscan:

1) Connect to multiple data sources and analyze their data in a

secure environment.

2) Find hidden insights to improve overall performance.

3) Identify patterns of cost and profitability.

4) Understand what are the challenges by department and/or

specialty.

5) Provide dashboards with beautiful visualizations to staff.

20 Things Healthcare Can Do With BI

6) Get automatic insights that can be easily turned into action to improve the

quality of patient care.

7) Do accurate allocation of resources.

8) Measure the impact of new programs and initiatives.

9) Monitor KPIs like patient wait times, bed rotation, nurse rotation, patient

volumes by time of the day or day of the week, etc.

10)Compare data to other hospitals per region using geo-analysis.

11)Analyze and compare costs of medical procedures by hospital, by region, etc.

12)Improve doctor and nurse productivity by generating automatic reports and

notifications, allowing doctors to focus on patient care rather than

paperwork.

13)Prioritize care for those patients who need it most.

14)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.

15)Manage cases efficiently by tracking care coordination interventions, care

transition assessments, and readmission risk.

16)Conduct hazard vulnerability analysis.

17)Analyze costs of different medical conditions. It is easy to analyze data of hospitalsby 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.

17)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 ofpatients most likely to be readmitted.

17)Drill down patient demographic data to discover hidden insights. For example: malepatients between the ages of 25-45 have spikes in cost in a certain period of time ina certain location. These insights can then be analyzed further to obtain moreinsights.

17)Cross analysis of patient readmission and different medical conditions. By analyzingtotal patient cost, readmission rates and diagnosis, we can find insights and trendsin the most susceptible patient groups and the most and least costly conditions totreat over time.

At the top of the list we mentioned that these things can be achieved using acentralized BI tool. This needs to be explained in more depth, because the right BImodel can ensure a BI project’s success in healthcare.

The correct implementation of a BIproject is a big challenge for any company.Healthcare organizations face an even biggerchallenge when implementing BI because ofthe nature of the data they handle. Theygenerate, collect, and analyze sensitive data,like patient records, patient financialinformation, etc. A lot of this data is regulatedby strict privacy rules. An example of this isthe Health Insurance Portability andAccountability Act (HIPAA), which calls forextra security and a special administration ofthe data.

Centralized BI in Healthcare

Only certain individuals are allowed tolook at private patient records. Healthcareorganizations have tried different models oforganizing their BI teams. Some havespecial BI teams under the supervision of aChief Medical Officer, a Chief FinancialOfficer and a Chief Operating Officer. Thismodel ends up creating silos. Each teamworks with a different person, each hastheir own data and work, edit, comment,and analyze only on their desktop. The datais siloed in archives controlled by differentadministrative 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 couldbenefit the whole organization.

The insights they find stay only in oneteam. Then it is impossible to try tomerge the data. And in these failedattempts to merge data, they realizethat there are many versions of thesame files… many versions of the“truth”. It is a data chaos. And we knowthat no business can allow data chaos,but in a hospital this might mean theendangering of lives. What if 10 peoplesave the same patient informationdifferently? Which one is the real one?

Federated = Data Chaos

It seems like having one version of thetruth and data accuracy plus data privacy isan impossible task. But it’s not. Informationneeds to be updated for everyone in onesame web solution, with IT overseeing whohas permissions to access it and who doesn’t.The best solution is for BI users to be able toanalyze the data in a centralizedenvironment.

Meaning they can share insights andresources between departments, but IT willgovern over the data (making sure to protectit and stand by HIPAA and otherregulations). The different users can benefitfrom the data, but IT will decide who gets tosee what and what is public and what isconfidential. Using a model like this ensuresdata privacy and one version of the truth. ITwill give the users the support and resourcesthey need.

Centralized = One Version of The Truth

Using centralized BI allows organizations toachieve consistency in data. Everyone in theorganization will be looking at the samenumbers, results, codes, practices, etc.There will be consistency of data inphysician masters, patient indexes andrecords, diagnosis codes, procedure codes,etc.

A good BI solution must provide an easy deployment, beautiful dashboards,top of the line analytics, automatic insights and KPI alerts; all in a centrallymanaged system. Users need to connect multiple data sources, consolidate andmashup all the data. They need to scale and deploy in a secure manner, in acentrally administered environment that does not require scripting. Usersneed to be able to automatically receive KPI alerts and notifications all the timeto be aware of what is happening in the business.

Necto offers all that, enhanced in a platform that allows you to collaborate,share, and discuss your findings. It uncovers the hidden insights in your dataand presents them in beautiful dashboards with infographics that users canunderstand better. It is an on premise solution with a web deployment, that iscentrally managed and secured.

Why Necto?