Introduction

Data provide the foundation for an evidence base in any field, including wound care. Common medical and nursing data sources include¹:

• Large public and private databases
• Clinical trials
• Insurance claims
• Electronic medical records (EMRs)

Experts evaluate data quality using levels of evidence as a standard.¹ “Big Data” is the term used to describe data sets too large for traditional software processing. Through big data, researchers have been able to predict certain clinical outcomes, such as the onset of sepsis.² As such, its emergence has transformed the way experts acquire and use health care data since big data analysis yields information that cannot be extrapolated from smaller, more specific data sets.²

Big Data Analytics and Machine Learning

Analysis of big data analytics is achieved through specialized tools and algorithms. In time, as more health care data are entered into large databases, big data analytics has the potential to identify patterns leading to new clinical insights.² Experts have hypothesized how big data can improve wound care practices for years. Now, through machine learning (ML), one can envision the prospect of clinicians using big data to predict the most effective and safest treatments for a particular patient.

ML systems resemble the human brain in that they learn from available data but with a difference in scope.² Whereas a clinician may have clinical experience with thousands of patients throughout a career, ML algorithms can integrate data from millions of patients. In terms of daily practice, ML can provide clinicians with useful diagnostic and therapeutic guidance drawn from that vast pool of data.²

Real-World Data Analysis

Real-world data analysis (RWDA) is an emerging field that may be a viable alternative to clinical trials because of its cost-effectiveness and precise representation of wound care patients.³ In RWDA, unlike big data analysis, data are usually collected from specialty (eg, wound care) EMRs, which contain more detailed information than general EMRs or broad databases. The specificity of RWDA gives it promising potential for meaningful clinical data application in wound care.³

Importance of Data in Wound Care Practice

Big data analytics may assess treatments received by millions of patients and may provide which treatment, product, or protocol had favorable outcomes. Experts have started using ML to make workflow improvements, for inpatient monitoring and outpatient communication, and for general hospital operations to increase the efficiency and efficacy of care delivery, as well as decrease complications.² It is theorized that big data and ML will present several applications for wound care professionals^2,4:

• Recommending safe and effective treatments
• Standardizing wound care practice
• Individualizing patient plan of care

One report described the case of a patient with an infected diabetic wound whose treatment regimen was changed several times in response to wound and laboratory data analyzed by a neural network and a wound EMR algorithm. As a result, the wound healed successfully.² In the future, these technologies are expected to lead to even more effective and more personalized patient care and better outcomes.²

Data in Wound Care Reimbursement

Data can play an essential role in how insurers determine wound care reimbursements. Some payors may factor in regional rates for a particular service, product, or procedure, and insurers may obtain this information from FAIR Health, an independent, not-for-profit corporation that is a large repository of private claims data in the United States.⁵ FAIR Health is accessible to both consumers and insurers.⁵

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Conversely, Medicare reimbursement rates take into consideration the recommendations of a committee comprising medical and other specialists.⁶ As a result, it is conceivable that robust and comprehensive data could play a role in future reimbursement structures, especially when factoring in the concept of value-based care.

Conclusion

In wound care as in all areas of life, knowledge is power, and the knowledge gained through big data analytics and machine learning may supercharge that power, with a goal of improving all aspects of wound care. Especially since the COVID-19 pandemic, digital devices, and other sources have become vital for data collection related to wound care. Although so far researchers have only been able to predict poor wound healing outcomes via big data, researchers urge that this emerging technology will be able to better predict specific wound care patient outcomes in the near future.²

References

1. American Association of Critical-Care Nurses (ACCN). ACCN levels of evidence. ACCN. Accessed January 2, 2024. https://www.aacn.org/clinical-resources/practice-alerts/aacn-levels-of-…
2. Woods JS, Saxena M, Nagamine T, et al. The future of data-driven wound care. AORN J. 2018;107(4):455-463.
3. Madu T. Conducting real-world data analysis in wound care – a guide. J Wound Care. 2021;30(2). Accessed January 2, 2024. https://doi.org/10.12968/jowc.2021.30.2.93
4. Queen D. Artificial intelligence and machine learning in wound care-the wounded machine! Int Wound J. 2019;16(2):311.
5. The Standard for Reliable, Secure Data. FairHealth. Accessed January 23, 2024. https://www.fairhealth.org/data
6. How does Medicare determine reimbursement rates? Medicare.org [nongovernmental organization]. Accessed January 2, 2024. https://www.medicare.org/articles/how-does-medicare-determine-reimburse…