The hype surrounding big data analytics in healthcare can be termed as challenging, but inescapable with the providers like Datafloq feeling the highest pinch of it. The need to reliably store the data today, safely and securely, and be sure that it will be efficiently accessible particularly useful in healthcare when needed adds to the excitement. Big data is long, complicated and bulky, often requiring taking a closer look at more vital aspects of it to ensure that it becomes meaningful.
Forget about the enormous enthusiasm regarding how big data will address continuous cost and quality deficiencies in the system, interpreting and successfully integrating them isn’t a mere walk. Clinical and IT departments with narrow focuses that solve a single problem at a time are the ones who feel the pressure even more. And those who have barely understood how to convert them into Electronic Health Records (HER) are now required to highlight actionable insights out of the data.
Clearly, the pathways to meaningful healthcare analytics are thorny ones even though some of the perks are healthier patients, lower healthcare costs, and higher consumer satisfaction. The facility will first have to collect, store, analyse, process and present the data to its stakeholders in a meaningful manner. It isn’t similar to providing medical attention, offering drugs and smart pills or attending to critical medical conditions.
The 10 Challenges Need Expert Analysts
To ensure that everything comes to a beautiful fruition, accuracy is each step of it is critical. Let’s look at the ten salient challenges organisations often face when it comes to initiating and running a big data analytics program.
1. Capture
As the first stage, data must be sourced from a place with unimpeachable data governance habits if it is to be truly clean, complete, well-formatted and accurate. It is a common issue for data to be skewed owing to the poor HER usability, elaborate workflows and little understanding of the essence of big data in the whole process. To solve this, however, there’s a need for providers to fine-tune their data capture routines, prioritise on valuable data, hire data governance experts and coach clinicians on how to make the information for analytics.
2. Cleaning
Cleanliness in the facility is of utmost importance, and the same is true with the data. If it is dirty, the data may ultimately derail the project. And so, manual cleaning done by IT vendors skilled in comparing and contrasting and correcting big datasets should be hired. It is only by so doing that accuracy will be achieved with integrity.
3. Storage
It is natural for front-line clinicians not to question where the data is to be stored. With the amount of healthcare data growing, however, some providers will strive to keep accommodating the immense costs by having a data centre. On-premise data centres guarantee easy access and control and security, but on-site server network can be expensive to scale and maintain and somewhat prone to sales across various departments.
Cloud storage is the norm nowadays, even in the healthcare sector. It offers quick disaster recovery, little up-front costs, and easier extension. The only problem, though is the need to choose a cloud partner that understands the essence of HIPAA and other healthcare-centred compliance and security matters.
At the moment, it seems a hybrid approach with regards to data storage is the way to go. It is very flexible, workable and provides varying data storage and access. The main problem, however, is to ensure that the systems communicate well and share data whenever it is needed.
4. Security
Storage isn’t adequate given the widespread cases of high-profile data breaches, hackings, and ransomware. The HIPAA Security Rule features a lengthy list of ways to safeguard Protected Health Information (PHI), including security during transmission, authentication, and regulation over access, integrity, and auditing. Using updated antivirus software, having a firewall, encrypting the data and employing multi-factor authentication seemingly does little to deter the vulnerabilities faced by data centres. And that’s why it takes regular reminding of a health organisation’s staff members on the need for data protection to ensure security all the time.
5. Stewardship
Healthcare data is expected to remain safely stored and evergreen, aside from being accessible for at least six years. Stewardship and curation thus become very important to help understand when it was created, by whom, why and who previously used it. An essential component of a good data governance plan is not just to develop complete and accurate metadata, but to also keep it up-to-date. A steward will be in-charge of assigning them standard definitions and formats, updating them and ensuring they are evergreen.
6. Querying
It is only through a robust and reliable stewardship that users will query the data and get meaningful answers. The ability to question is a fundamental aspect of reporting and analytics, including overcoming any challenges faced along the way. Data siloes and interoperability problems that inhibit access to a repository of information must be overcome.
Even when the data is available in a conventional warehouse, standardisation and quality may lack. The mere absence of medical coding systems such as ICD-10, SMOMED-CT and LOINC might make it hard to return the desired result. And that’s why SQL is a standard querying tool in large databases in healthcare centres.
7. Reporting
Everything done culminates to a report that must be clear, concise and accessible to all who need it. Still, accuracy and integrity from the first stages matter in how reliable and accurate the report will be. Important to note, though, is that reporting is a prerequisite for analysis and that reporting can be on its own as a final product.
The report must be used to highlight a given trend, show a conclusion, convince the target to take a specific action or initiate the reader’s independent findings. It is the initial plan that dictates how database administrators will generate the information they’ll need.
Most of the reporting in the healthcare sector is geared towards the world since regulatory and quality assessment programs call for it. And so, providers are free to use different requirements, including certified registries, reporting programs integrated into their database records or web portals hosted by CMS.
8. Visualization
Clean, clear data visualisation is vital for comfortable understanding and absorbing the information in a clinic. It could be colour-coding using common colours; red, yellow and green for stop, caution and go. Excellent data presentation techniques like the use of charts and convoluted flowcharts help reduce any confusion. Heat maps, pie charts, bar charts, histograms, and scatterplots will help illustrate concepts and information.
9. Updating
Healthcare data is dynamic, and most of the elements involved will need regular updating. Basing on the volatility of big data, some may happen in seconds while others will change a few times during the lifetime of an individual. It is, therefore, upon the providers to discern which datasets to update when and if they can be automated or not. In everything, however, caution must be taken to ensure unnecessary duplication of records.
10. Sharing
It is the vital part of this process to share data with external partners, given that the industry is edging towards health management and value-based attention. There are several impediments to sharing, including data interoperability, how electronic health records are presented and the way clinicians will understand them.
The good news, though, is how the whole industry is working hard to improve sharing of data over technical and organisational obstacles. Such tools as FHIR and Public APIs alongside partnerships with CommonWell and Carequality are making it easier to share data securely too.
