by Hy-Tape International
Wound Care Devices, Apps, Integrations, and Analytics: A Digital Health Platform Overview for Industry and Clinicians
by Rafael Mazuz
Computer vision, machine learning, Electronic Medical Record (EMR) integrations, clinical decision support -- a new class of digital health technologies are transforming the practice of advanced wound care. In this article, we’ll explore the significance of this relatively new yet crucial dimension for wound care stakeholders by focusing on four major categories:
- Advanced sensors and imaging devices
- Mobile measurement apps
- EMR connectivity and integrations
- Data analytics and predictive modelling
1) Advanced Sensors, Diagnostics, and Imaging Devices
Relative to other medical specialties, advanced wound care has not fully leveraged the capabilities of diagnostic technologies. While standard biopsies and tissue imaging are common, tools developed specifically for wound care are rarely used. This is despite the increasing number of pressure and temperature sensing mats, footwear, probes, and dressings intended to diagnose and monitor pre-wound, wound and related conditions. Some notable examples of products that fall in this category include:
Table 1: Examples of advanced wound care sensors, diagnostics, and imaging solutions launched in recent years
There is consensus that these technologies have the potential to improve wound healing, which begs the question; why are most still struggling to gain traction outside of clinical trials and well-funded academic institutions?
One explanation is that aside from the basic business model challenges of cost and reimbursement, a major barrier to mass adoption of technological products is connectivity. Both manufacturers and providers are hesitant to expend the capital and time resources needed to plan and implement custom EMR integrations for data capture and workflow efficiency. A streamlined process to input data into the medical record is essential for these sensors, diagnostic and imaging devices in being able to influence clinical decision support systems.
2) Mobile Measurement Apps
The proliferation of digital cameras and smartphones has resulted in a special subset of applications making visual approximations, measurements with paper rulers, and swabs outdated. There are now multiple companies that provide faster and more accurate mobile measurements through a smartphone application. Tissue Analytics has even developed the capability to capture depth measurements using just a smartphone and no external hardware. However, while this speeds up the capture of wound measurements, the time savings are lost when one accounts for the time a busy clinician needs to log into a separate EMR system to upload files and other documentation on the wound. Additionally, individual facilities and providers will likely continue to push back on the type of transparency, accountability and privacy issues that go along with mobile solutions. Similar to the diagnostic devices previously discussed, the true potential of mobile measurement apps remains untapped unless they are part of a larger EMR integration and analytics platform.
3) EMR Connectivity and Integrations
While many digital health products have an impressive feature list, the lack of EMR connectivity impedes utilization. On the other hand, proper EMR integration enables the use of advanced device features by not just one wound care team but every clinician and administrator in the health care system. A good EMR integration eliminates the need for clinicians to choose between spending time with patients and performing documentation tasks. It also reduces the errors from double documenting while making the data easily accessible for future analysis. EMR integration is key to linking wound-related data to treatments and outcomes. This includes features such as height, weight, comorbidities, medical-surgical history, and many others that may not be otherwise documented. As the saying goes, “Treat the whole patient, not just the hole in the patient”. EMR integration provides the missing “w.”
A great example of wound care EMR integration involves Intermountain Healthcare, a forward-thinking integrated delivery network (IDN) in the Western US, and Tissue Analytics. Intermountain prides itself on the interconnectivity of its sites and services but like most IDN and health care facilities, they saw wound management as a huge opportunity for simultaneously improving patient care and streamlining operations. Through seamless EMR integration, they were able to successfully connect their inpatient wound care team with home health. By having a solution like Tissue Analytics in use throughout their multiple facilities, health care systems such as Intermountain can also integrate next generation mobile diagnostics and treatments.
4) Data and Predictive Modeling
Fig. 1: Dataset, patient and wound characteristics. A) The dataset is drawn from 11 facilities over a period spanning 2015-2018 and comprises 3470 wounds from 1739 patients. Patient and wound information was collected and recorded using Tissue Analytics’ EMR-integrated Mobile Wound Care application. We used information recorded in the first two assessments to predict delayed wound healing. The mean(median) time between the first and second assessments was 12.7 (7.0) days. B) Number of wounds by etiology. C) Distribution of patient age, binned by decade. D) Number of days until wound status analysis, in 28 day bins. The threshold for delayed wound healing is 105 days. E) Distribution of wounds binned by visit frequency.
Fig 2: Healing vs. Non-healing comparison. A) Healing vs. Non-healing for the total dataset. B) Median visit frequency for Healing vs. Non-healing wounds. C) Median age for Healing vs. Non-healing wounds. D) Healing vs. Non-healing wounds by facility type. E) Healing vs. Non-healing wounds by etiology.
Fig 3: Model Evaluation. A) This model achieves an AUC curve of 0.72 with cross-validation. B) The positive slope of a representative learning model shows that more data will improve the model. However, there might be more gains to be made by feature engineering to get higher AUC scores. C) Area reduction is calculated as the % reduction in wound or tissue area between the 1st and 2nd wound assessment.
With the right devices integrated in an EMR platform, this paves the way for advanced analytics. It is no longer compelling to approach clinicians with product efficacy tests where the results were “patients receiving product X healed 15% faster than those receiving product Y.” This is because:
- There are often big variances in the clinical trial designs (patient selection, standard of care, etc.), and clinicians and administrators know this
- Advanced wound care product customers are inundated with these types of claims, yet rarely read the actual studies cited
- Traditional (ruler) measurements are very subjective
- There is often a counterclaim study, “patients receiving product Y healed 20% faster than those receiving product X.”
A more convincing marketing pitch for future pre- and post-market clinical studies will be closer to:
“Patients under 60 years old, who do not smoke, with a BMI of 25 or higher, and a wound located above the knee, are more likely to benefit from Product X in the first four weeks of treatment, while Product Y is more effective in those same patients below the knee when the wound size is improving less than 5% per week after week three…”
In simple terms, advanced sensors and imaging devices, mobile measurement apps, and EMR connectivity and integrations are inputs which individually do not significantly raise the bar for advanced wound care specialty. However, when combined into a platform, they can elevate the science and delivery of this specialty to meet or even exceed the level found in best-in-class disciplines that leverage digital health.
Unlike companies in other healthcare sectors, none of the incumbent advanced wound care firms have any type of impressive competency in digital health. Yet despite differences in their individual approaches, most of the leading firms have determined that digital health needs to be a key part of their overall strategy, even if that strategy takes on different forms, such as:
- Driving operational efficiencies for clinicians using their product(s)
- Improving scale and impact of clinical trials while vastly reducing costs and delays
- Insulation from reimbursement, regulatory, and product commoditization pressures
- Risk-sharing with customers and payers
- Informing their in-house new product R&D and licensing / M&A pipeline strategy
- A tool to enter new markets and drive branding and customer relationships
Companies who want to evolve with the changing digital wound care landscape may not need to invest years and millions of dollars to build and maintain an in-house digital health division. There exist opportunities for firms to “get in the game” of digital health, while staying true to their overarching commercialization strategy. Some recent examples include:
- Integra LifeSciences’ (NASDAQ:IART) partnership with Tissue Analytics to perform cutting edge clinical trial data collection
- Wound Care Advantage’s partnership with Tissue Analytics to create opportunities for better data insights
- Mölnlycke Health Care’s strategic digital health equity investment in Tissue Analytics
- Sanuwave’s collaboration with Tissue Analytics to quantify the known--and potentially yet undiscovered--benefits and treatment algorithms for wounds, like DFUs, with its newly launched dermaPACE shockwave therapy system
- Zuellig Pharma Asia, a large international medical distributor, using Tissue Analytics to raise the level of patient care, customer support, and to generate data for a new product launch across eight countries
There exist dozens of standalone digital health devices and apps on the market. Yet as most wound care firms would attest, incorporating digital health into a unique long term strategy is about more than having a handful of diagnostics, therapies, data sets, or measurement tools.
Even though most industry partners choose to utilize Tissue Analytics data to fuel their clinical trials due to its measurement reliability, some firms may leverage multiple avenues for acquiring large, deidentified patient/outcome data sets, including from sources such as:
- CMS, NHS, or other national diagnosis, treatment, and claims databases
- Specialty EMRs like NetHealth, IntelliCure, and others such as specialty nursing home, home health, or primary care EMRs
- Data sets internally generated over periods in time (for example, combining dozens or hundreds of clinical trials from over the years)
- Facilities or IDNs, especially those using high volumes of their products/services (including before-and-after data) or that they operate through partnerships outside the US
Tissue Analytics has emerged as the leader in this space, with dozens of industry partnerships and hundreds of facilities including large hospital IDNs such as Intermountain Healthcare, Penn Medicine, and Bayfront Health. Their successful integration with the largest EMRs (Cerner, Epic, Athena, and Allscripts), a growing wound registry, and data capture/analysis capabilities is demonstrating the need to go beyond just mere devices and apps. In the coming months, we will continue to see more wound and skin care players join the Tissue Analytics ecosystem.
We are seeing a monumental shift unfold: As more leading care sites and product firms engage in strategic digital health partnerships, the value of the inputs (data) and outputs (insights) is growing exponentially. Wound care executives, clinicians, and organizations without a clear digital strategy incorporating all four elements: 1) connected, advanced devices/diagnostics; 2) mobile measurements; 3) EMR integrations; and 4) data and predictive modeling will risk becoming commodities and even barred from participating in/selling to entire segments of the ecosystem. On the other hand, those who plan and execute within that key framework are poised to reap enormous benefits.
About the Author
Rafael Mazuz is the Managing Director of Diligence Wound Care Global, and an Independent Board Member for Tissue Analytics. He previously spent over five years opening, directing, and mentoring wound care centers for a large US wound management firm.
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