5 Hurdles Faced by Data Analytics Programs in Healthcare

It is a known fact that wisdom that has been extracted from years of accumulated data, plugged into clinical and administrative procedures can prove extremely rewarding for all kinds of healthcare entities. However, several challenges mar the road to a successful application of data analytics methods. Read on to learn more about these roadblocks that make it difficult for many organizations to squeeze out the maximum benefit from their stored records.

While big data as a concept sounds very intriguing, especially in the healthcare sector, rolling out an analytics system is a task that is rife with complications. In an ideal world, data is collated, stored, investigated, and exhibited correctly, giving stakeholders the much needed fact-based feedback that can be used to improve processes and procedures across the gamut of activities performed within a healthcare setup. Unfortunately, though, we do not live in an ideal world. In reality, there are a host of issues that crop up at every stage of data management, making it tougher for an analytics program to run at full throttle and generate usable results.

Here are the five most persistent problems that prove to be detrimental to the efficiency of a healthcare-related analytics program of any size:

  1. Unsuitable Data Capturing Methods –Availability of data is of utmost importance for any data analytics system to work well. The inability to timely capture accurate data and ensure that it is complete in all regards is the main reason behind a system breakdown. Under-utilized EMR systems, workflows that are either broken or too complex, and a lack of interest and knowledge amongst system users, are issues that lead to poor data quality and, therefore, poor results.

  2. Poor Data Quality – Consistency, relevance, and precision of data are aspects that are non-negotiable when it comes to analyzing such data. Even though cleanliness is a concept that should ideally come naturally to healthcare workers, when applied in the context of data sets, it is sadly forgotten. Data integrity issues can be a big reason for the failure of analytical systems to produce meaningful results.

  3. Data Storage-Related Issues – While the issue of data storage and the costs emerging from it has gone down considerably with the advent of electronic databases; healthcare facilities continue to grapple with storage problems. Which storage technology to use, how to ensure interoperability and smooth data flow between incompatible systems, and how to build sufficient knowledge in the area of compliance and security related to electronically stored data are a few examples of the dilemmas that hound data managers in healthcare units today.

  4. Lack of a Sound Data Security Framework – Data security becomes a much bigger issue when we are talking about healthcare-related data. In the wake of a slew of cybercrimes that have occurred of late, including account hacking, ransomware incidents, and database breaches, healthcare facilities are finding themselves increasingly vulnerable to data theft. Human errors by omission or commission are the top reason behind such attacks. It is, therefore, vital to define data security procedures that are as close to being infallible as possible and have them reviewed by data security experts to identify gaps and suggest fixes.

  5. Disintegrated Data Sets – For an analytics and reporting system to work as expected, it is vital for it to have access to the entire data bank of the organization. In large entities where different data pieces reside in non-interconnected, diverse systems across departments, it is next to impossible for query tools to acquire the entire information needed to give the expected answers.

Mirra Health Care is an organization that employs experienced data analytics experts who can help you clean up your data management processes so that your facility can produce good quality data that can then be appropriately analyzed using big data techniques. Our professionals can also guide you on how to establish a data security strategy that is iron-clad and effective in protecting you from cyber-attacks.

 

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