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With the rapid rise of Electronic Health Records (EHRs) in healthcare, the creation of big data, its use, and EHR data analytics have become commonplace. The reality is that the healthcare industry—in particular long term care—stands to benefit from big data analytics, as medical data is incredibly complex and contains a wealth of information about patients, diseases, treatments, and outcomes.

The term “big data” has been used in business and technology circles for years, but its meaning is still somewhat nebulous. Big data refers to the increasing volume, velocity, and variety of data organizations deal with on a day-to-day basis. This large and diverse data set can be used to gain insights into trends and patterns.

In long term care, data analytics are made possible by way of long term care software. According to a recent report by Analytics Insight, the healthcare industry is expected to see a compound annual growth rate (CAGR) of 36% in big data through 2025. And this rapid growth is largely due to the increasing adoption of electronic health records (EHRs), which generate large amounts of data (big data) that can be used for analytics.

EHR data analytics have the capability of extracting, cleaning, and analyzing data about a patient or resident, including diagnoses, medications, lab results, and more. The way it works is that EHR data analytics mine patient or resident data for insights that can improve their care. For example, analysts may look for patterns of disease progression to identify early warning signs that could help physicians intervene sooner. Alternatively, they may use the nursing home software to examine treatment data to find more effective methods of care.

Benefits of Big Data in Healthcare

Nursing homes and other long term care facilities are under constant pressure to improve resident outcomes while reducing costs. And big data analytics has the potential to help meet both objectives by providing insights that can enhance workplace efficiency and quality of care.

Some of the benefits of big data in healthcare include:   

A SNF taking measures to prevent disease spread, thanks to EHR data analytics.
EHR data analytics can be used to track disease spread amongst residents and identify potential risk factors within a SNF.
  • Reducing medical errors: Big data can identify patterns that may indicate care plan errors, such as incorrect medication dosage. This helps facilities put processes in place to reduce the likelihood of such errors.
  • Preventing mass diseases: EHR data analytics can track disease spread amongst residents and identify potential risk factors within a Skilled Nursing Facility (SNF). Nurses can then use this information to develop prevention and treatment plans.
  • Preventative care: Nurses can use EHR data analytics to identify residents who are at risk for certain infections and develop strategies to prevent them.
  • More accurate treatment: Thanks to efficient point of care charting software, nurses and physicians can better understand which treatments are most effective by analyzing data from previous residents with similar conditions. Long term care facilities can then use this information to tailor effective treatment plans for seniors, resulting in improved health for all its residents.
  • Predicting the cost of treatment: Big data can predict the cost of future treatments by analyzing data on past treatments. This information can help SNFs budget for care and make informed decisions about the most cost-effective treatments.
  • Identifying and assisting high-risk patients or residents: EHR data can be used to identify residents who are at risk for falls or other dangers. SNF staff can then use this information to develop a care plan that includes fall prevention or other preventative measures.
  • Prevention of unnecessary emergency room visits: For long term care facilities, a resident’s re-admission to a hospital can be perceived as a sign of inadequate care. Big data can be used to identify patterns that may indicate a need for an ER visit, such as a sudden change in vitals. By catching these signs early, nurses can provide the necessary care and avoid unnecessary ER trips.
  • Improved staff management: Administrators can use big data to track staff performance and identify areas where additional training may be needed. They can then use this information to improve the quality of care by ensuring that employees are adequately equipped to meet the needs of residents.
  • Cost reduction: Big data can help healthcare facilities save money by identifying healthcare and medical data patterns and improving efficiencies.

Contact us here if you would like to test drive our user-friendly long term care software.

9 Applications of Big Data in Long Term Care

Having discussed some of the benefits of big data in long term care, as well as how nursing homes and other long term care facilities can make sense of their EHR data analytics, it is worth discussing how big data can be applied in long term care. The real-world application of big data in nursing homes, assisted living, and other long term care facilities include:

One of the benefits of big data in healthcare is that it facilitates telemedicine.
Telemedicine is a type of remote care that uses technology to connect patients with providers remotely.
  1. EHRs: EHR data collection is the process that produces big data in healthcare. EHRs are the largest application of big data in healthcare, with nurses and physicians enjoying access to real-time, accurate patient/resident documentation.
  2. Real-time alerting:  By analyzing EHR data, long term care facilities can set up alerts that notify staff of changes in residents’ conditions. Staff can then use this information to provide the necessary care and avoid potential complications.
  3. Strategic planning: Big data can help facilities plan for the future by identifying trends and patterns. This information can help long term care facilities budget for care and make informed decisions about the most cost-effective treatments.
  4. Predictive analytics: Predictive analytics is a type of big data application where historical data is used to predict future events. Using this information, nurses can identify residents at risk for certain infections and develop strategies to prevent them.
  5. Fraud reduction and enhanced security: Financial fraud is a serious concern in the healthcare industry. EHR data analytics can be applied together with big data to identify patterns that may indicate fraudulent activity. This information can then be used to improve the financial security of long term care facilities.
  6. Telemedicine: This is a type of remote care that uses technology to connect patients with providers remotely. EHR data facilitates telemedicine by providing information about a patient or resident’s health history and current condition. This enables healthcare providers to deliver medical consultations and other medical services remotely.
  7. Smart staffing: As the staffing crisis persists, long term care providers need to get creative with handling their staffing shortages. Big data can identify patterns in employee behavior, which administrators can then use to improve staff management and create a more efficient workplace.
  8. Supply chain management: Like other industries, supply chain management is critical in long term care. One of the benefits of big data in healthcare is that it helps track the utilization of supplies and equipment by streamlining the supply chain and reducing costs. A materials management module automates the process of purchasing, receiving, and scanning orders for everything from paper clips to IV bags. 
  9. Development of new therapies: The future of big data in healthcare lies in its ability to assist with the development of new therapies and treatments. EHR data, in particular, can be beneficial in this area. By analyzing historical data, experts can identify patterns indicative of potential problems or areas for improvement. In addition, facilities can use EHR data analytics to generate real-time alerts that can help clinicians spot potential issues early on.

How to Use EHR Data Analytics Most Effectively

While the use of EHR data analytics is growing, it is not without its challenges. One such challenge is the lack of standardization and integration in EHR data. This can make it difficult to compare data across different EHR systems. 

A nurse and a physician using big data in long term care
It’s worth noting that a lack of trained personnel can limit the ability of healthcare organizations to benefit from EHR data analytics.

Another challenge is the need for skilled personnel to develop and interpret the data. A lack of trained personnel can limit the ability of healthcare organizations to benefit from EHR data analytics. A recent article highlighted the challenges of data overload if not handled correctly. Some of those challenges were related to: 

  • Setting priorities
  • Commingling data
  • Checking KPIs
  • Accessing data

But those issues can be overcome. To ensure big data is used properly in their organizations, administrators should:

  • Place data analysis within workflows: Data analysis should not be done in a vacuum but rather integrated into the facility’s EHR workflow. This will help ensure that data is utilized to its full potential and provides insights that can help improve care quality and efficiency.
  • Look for actionable data: Not all data is created equal, with some data being more crucial to resident care than others. When reviewing data, a director of nursing should look for trends and patterns that can be acted upon by nurses. This data can help improve operational processes and resident care.
  • Focus on metrics that matter: In data analytics, various metrics can be tracked, but not all will be relevant to every facility. Administrators should focus on the metrics that are most important to their facility and align them with their goals.
  • Make data available to the relevant staff: Data is only helpful if available to the staff who can need it. Therefore, administrators should ensure that EHR data analytics results are accessible to the relevant staff members so they can use them as needed.
  • Leverage collective intelligence: EHR data analytics is not a one-person job. It should be a team effort involving input from various departments. By leveraging the collective intelligence of the facility, administrators can make better decisions about care quality and operations.

For more on recent trends in long term care, read our blog and subscribe to the LTC Heroes podcast.

Elijah Oling Wanga
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