5 challenges healthcare industry face during big data analytics

5 Challenges the Healthcare Industry Could Face During Big Data Analytics There are providers of data cleansing services that can cleanse the data and ensure that the data sets are accurate, consistent, relevant and not corrupted.

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The healthcare industry is highly paper-oriented as they deal with various documents like medical history, patient records, HIPAA forms, insurance claims and other legal documents that have to be retained for 10 years atleast. Healthcare organizations are in search of methods that can lower the cost and ensure better outcomes. As a part of cost cutting, healthcare organizations are now turning digital with the support of data conversion services. Data is important for any business, including the healthcare sector.

In fact, healthcare providers are now expected to extract actionable insights from their data and apply that information to complex initiatives that have a direct impact on their reimbursement rates.

Health organizations can gain huge rewards if they successfully integrate data driven insights into their clinical and operational process.

These rewards include healthier

patients, lower costs, clearer understanding of performance, and increased consumer and staff satisfaction rates.

Healthcare analytics is not an easy task, this is because big data is very complex and unwieldy. It requires a close look at their approach to collecting, storing, analyzing and presenting their data to staff members, business partners, and patients. The following are some of the following challenges and problems to solve. •

Capturing data: In a healthcare setup data comes from various sources that do not have good data governance habits. The captured data should be clean, complete, accurate and formatted correctly for using it in multiple systems. In a recent study at an ophthalmology clinic, EHR data matched patient reported data in just 23.5 seconds but when patients reported having three or more eye problems, their EHR data did not agree. Providers can improve their data capture by prioritizing valuable data types for specific projects, enlisting the data governance

and

integrity

expertise

of

health

information

management

professionals and develop clinical documentation improvement programs that can train clinicians about how the data is useful for downstream analytics. •

Storing data: Clinicians do not give much importance to storing of data but it is important for the IT department. As the healthcare data volume grows, it becomes difficult to handle the overflowing data. Majority of organizations are comfortable with on premise data storage which ensures control over security, access and up time, but an on-site server network can be expensive to scale, difficult to maintain and prone to producing data siloes across different departments. Cloud storage has become very popular now and it is an excellent option to reduce cost. Almost 90 percent of healthcare organizations are using

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cloud-based storage for storing all critical data. It also offers nimble disaster recovery, lower front cost and easy expansion. Many healthcare organizations choose a hybrid approach of storing data and it is a flexible and workable option.

However,

providers

should

ensure

that

disparate

systems

are

able

to

communicate and share data with other segments of the organization when necessary. •

Security of data: Data security is one of the most important criterion for healthcare organizations. The HIPAA security rule includes a list of technical safeguards

for

organizations

storing

protected

healthcare

data

including

transmission security, authentication protocols and control over access, integrity and auditing. Use of antivirus, setting up firewalls, encrypting data and using multi factor authentication are some measures to protect data. But sometimes highly secure data can also be taken down due to fallibility of human staff. So healthcare organizations should regularly remind their staffs about the data security protocol and constantly review who is accessing the data to prevent malicious practices. •

Data cleaning: Healthcare providers are aware of data cleansing and its importance. Poor quality data can lead to poor data analytics project. Especially when disparate data sources are brought together that may record clinical or operational elements in a different format. You can use data cleansing services that can cleanse the data and ensure that the data sets are accurate, correct, consistent, relevant and not corrupted. Some IT vendors offer automated scrubbing tools that use logic rules to compare, contrast and correct large datasets. Sometimes the data cleansing process is still done manually but IT vendors offer machine learning techniques that reduce time and expense and also ensure high level of accuracy and integrity of healthcare data.

•

Stewardship: Healthcare data of patients need to be stored for a long period of time, at least for 6 years. Providers may wish to utilize the identified data sets for research projects, which makes ongoing stewardship and curation an important concern. The data can be reused or re-examined for other purposes like quality measurement or performance evaluation. It is important for researchers and data analysts to know who created the data, for what, who previously used the data, why, when, how and so on. Developing complete, accurate and up-to-date meta data is the key to a successful data governance plan. Metadata helps analysts to replicate previous queries which is important for scientific studies and accurate benchmarking, and prevents the creation of isolated datasets that are limited in their usefulness.

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Big data is generating a lot of hype in almost all industries and more and more healthcare organizations are leveraging big data technology to capture all patient information. The goal of this technology is to get better insights that can aid in diagnosis and treatment for patients. So it is important for healthcare organizations to maintain their patient data in digital format with the help of data conversion services. In-housing data conversion is expensive as it requires high speed scanners for scanning huge volumes of data, advanced software and above all an experienced team who can scan medical documents with utmost accuracy. Assigning the scanning process to the hospital staffs will affect their working hours and it can lead to reduced productivity. Hence outsourcing to an established service provider ensures accurate digitization of medical records that can be stored and retrieved easily.

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