Data generated through PGHD captured via mHealth for SSI surveillance should be leveraged to better characterize the natural history of SSI and inform a review of current clinical and public health practices and surveillance standards for identifying and diagnosing SSI.
The creation of a database of patient-generated wound photos presents a unique opportunity to utilize a large volume of post-operative wound data for advancing SSI research. Of primary importance is leveraging patient-generated wound photos to increase what known about the natural history of post-operative wound healing, inclusive of normal healing processes, complications of wound healing, and SSI. Serial wound photos submitted by patients provide a longitudinal view of the process of wound healing in a novel way that is not otherwise possible given current clinical practice and research procedures that produce only intermittent data during the post-operative period.82 Characterizing the natural history of SSI is in turn critical to a needed review of current clinical practices and surveillance standards for defining SSI.83 While current surveillance standards inform clinical practice, gaps exist between accepted national guidelines84,85 and how they are applied in the context of care delivery.86-88 These challenges persist for a variety of reasons including lack of consensus around what criteria are both sufficient and necessary for diagnosing SSI in the clinical context, and inconsistent interpretation of those criteria which may vary by the individual applying them.89-93 The literature on PGHD captured via mHealth for SSI surveillance reflects this; definitions of SSI are neither uniformly agreed upon nor consistently reported across research projects.94-95 Further, currently available mHealth tools for monitoring SSI do not apply standardized metrics for evaluating normal vs abnormal wound healing. Serial wound photographic data should be leveraged to address these issues, increase the breadth and depth of what is known about surgical wound healing, and inform the development of best practices and standards for the surveillance and clinical diagnosis of SSI.
In leveraging patient-generated wound photos captured via mHealth for SSI in research, consider the following:
Current data sets are likely biased toward over-representation of patients diagnosed with SSI. In order to fully characterize the natural history of post-operative wound healing it is necessary to include data from the broadest possible sample of patients. This must include patients who have experienced post-operative wound complications, as well as those who have not. This will ensure that the data is not biased toward particular outcomes or complications, and will reflect a more accurate view of post-operative wound healing.
Additional clinical data may be needed to develop a definition of SSI that is most accurate. Photographic data may best be utilized when paired with other data including type of surgery, demographic information, vital signs, wound cultures, and additional patient-generated data. It is important to consider which data points are necessary to support the use of serial wound photos for a review of current definitions of SSI.
There is a need to standardize data formats of photos submitted by patients. Standardization of data formats allows for image processing utilizing advanced analyzation techniques, for example computer vision, which uses pixel-by-pixel analysis and may be negatively impacted by low photo quality.