Periodic Reporting for period 1 - PERISCOPE (Perioperative infection prediction)
Periodo di rendicontazione: 2019-06-01 al 2019-11-30
Some 50M people need to undergo inpatient surgery per year in Europe Yet, despite intensive research efforts, around 25% of surgical patients will experience a complicated recovery with some type of infection on their path: pneumonia, urinary tract infections, wound infections, abdominal infections and bacteremia. Of course, the risk varies depending on the patient and the procedure, but despite efforts like biomarkers, risk scores or devices for early warning/detection, and preventive methods like ‘Enhanced Recovery After Surgery’ (ERAS), the overall risk still leaves almost one in four patients to get an infection within 30-days after surgery.
If we look beyond the personal suffering of patients and relatives, the cost of an infectious complication is estimated at around €10,000 per patient. Hence, total cost in only the Netherlands alone can be calculated at up to €3.5B per year, as approximately 350,000 Dutch patients go through an infection after surgery yearly. Total EU costs are even more shocking at an estimated €125B per year.
Healthplus.ai R&D BV is currently developing an advanced machine-learning (ML) algorithm (TRL level 4) to predict post-operative infections 5 days prior to the average medical team diagnosis, currently already achieving an accuracy of 80%.
Through increasing the dataset and taking on more types of data, the ultimate goal is to ultimately achieve >90% accuracy at 5 days before the infectious symptoms on average are actually detected within the patient. In this proposal, we seek how to go beyond the proven technical feasibility and assess channels to deliver the tool in a safe, affordable and scalable way through third-party vendors with a sustainable business model and plan with the right partnerships and intensify these relationships. Also, potential legal and regulatory issues with EU expansion will be identified, investigated and suitable measures outlined.
If we look beyond the personal suffering of patients and relatives, the cost of an infectious complication is estimated at around €10,000 per patient. Hence, total cost in only the Netherlands alone can be calculated at up to €3.5B per year, as approximately 350,000 Dutch patients go through an infection after surgery yearly. Total EU costs are even more shocking at an estimated €125B per year.
Healthplus.ai R&D BV is currently developing an advanced machine-learning (ML) algorithm (TRL level 4) to predict post-operative infections 5 days prior to the average medical team diagnosis, currently already achieving an accuracy of 80%.
Through increasing the dataset and taking on more types of data, the ultimate goal is to ultimately achieve >90% accuracy at 5 days before the infectious symptoms on average are actually detected within the patient. In this proposal, we seek how to go beyond the proven technical feasibility and assess channels to deliver the tool in a safe, affordable and scalable way through third-party vendors with a sustainable business model and plan with the right partnerships and intensify these relationships. Also, potential legal and regulatory issues with EU expansion will be identified, investigated and suitable measures outlined.
To achieve our main objective: “To further study the commercial, legal, governance and (corporate and clinical) partnership framework with specific focus on the UK, Spain and Sweden for bringing our PERISCOPE tool, which predicts post-operative infections by re-using existing hospital data, and investigate IPR options” we performed the following work:
1. Commercial feasibility:
• In-depth interviews were held with end-users (surgeons, infectious disease specialists, microbiologists, infection prevention specialists, junior physicians, physician assistants and nurses).
* We identified how we can deliver on transparency needs of the professionals and how they can leverage these insights to be more effective in their work.
• We interviewed other stakeholders from patient organizations, insurance companies, ministry of health regulators to identify hidden risks, barriers, opportunities.
• We spend a lot of time researching technical, functional, operational, financial, sales and marketing backgrounds for our competitors beyond our current patent and competitor search.
2. Partnership assessment:
• We had many interviews and in depth discussions with Electronic Health Record vendor to assess for options for partnerships and third-party vendor agreements
• These interviews gave us a lot of insight into the technical requirements for EHR-plugin or EHR-marketplace cooperation, processing time scenarios and operational requirements for future pipelines.
• These interviews allows us to close the loop we need feedback from members of medical scientific associations, which can provide with input on the user experience and important tools and measurement standards that need to be added.
• We participated in many conferences on general and other high-risk surgeries, sepsis-oriented intensive care meeting
3. Regulatory
• We analyzed the regulatory claims that we will need to make and underpin based on our prior interviews, our technical results and the outcomes from the functional requirements assessment.
* We had many in depth discussions with hospital lawyers and privacy officers to determine the regulatory bounds and limits.
4. Others
• We examined patents databases and online research for patents matching the patent we are planning to enter so that we can identify the path and any obstacles for patent applications.
• We scouted for additional sources of data with different universities in Europe: eg. University of Tromsø.
• With experts we examined potentially relevant ethical issues around our tool and to determine the ethical boundaries of our AI tool.
1. Commercial feasibility:
• In-depth interviews were held with end-users (surgeons, infectious disease specialists, microbiologists, infection prevention specialists, junior physicians, physician assistants and nurses).
* We identified how we can deliver on transparency needs of the professionals and how they can leverage these insights to be more effective in their work.
• We interviewed other stakeholders from patient organizations, insurance companies, ministry of health regulators to identify hidden risks, barriers, opportunities.
• We spend a lot of time researching technical, functional, operational, financial, sales and marketing backgrounds for our competitors beyond our current patent and competitor search.
2. Partnership assessment:
• We had many interviews and in depth discussions with Electronic Health Record vendor to assess for options for partnerships and third-party vendor agreements
• These interviews gave us a lot of insight into the technical requirements for EHR-plugin or EHR-marketplace cooperation, processing time scenarios and operational requirements for future pipelines.
• These interviews allows us to close the loop we need feedback from members of medical scientific associations, which can provide with input on the user experience and important tools and measurement standards that need to be added.
• We participated in many conferences on general and other high-risk surgeries, sepsis-oriented intensive care meeting
3. Regulatory
• We analyzed the regulatory claims that we will need to make and underpin based on our prior interviews, our technical results and the outcomes from the functional requirements assessment.
* We had many in depth discussions with hospital lawyers and privacy officers to determine the regulatory bounds and limits.
4. Others
• We examined patents databases and online research for patents matching the patent we are planning to enter so that we can identify the path and any obstacles for patent applications.
• We scouted for additional sources of data with different universities in Europe: eg. University of Tromsø.
• With experts we examined potentially relevant ethical issues around our tool and to determine the ethical boundaries of our AI tool.
We will progress beyond state of the art. Our current state is the art is an advanced machine-learning (ML) algorithm (TRL level 4) that re-uses available EHR (Electronic Health Record) data to predict post-operative infections 5 days prior to the average medical team diagnosis, which achieves an accuracy of 80% (training set 40,000+ patient operating room data records). Through increasing the dataset and taking on more types of data, the ultimate goal is to ultimately achieve >90% accuracy at 5 days before the infectious symptoms on average are actually detected within the patient.
Some 50M people need to undergo inpatient surgery per year in Europe. Yet, despite intensive research efforts, around 25% of surgical patients will experience a complicated recovery with some type of infection on their path: pneumonia, urinary tract infections, wound infections, abdominal infections and bacteremia. Of course, the risk varies depending on patient and procedure, but despite efforts like biomarkers, risk scores or devices for early warning/detection, and preventive methods like ‘Enhanced Recovery After Surgery’ (ERAS), the overall risk still leaves almost one in four patients to get an infection within 30-days after surgery.
If we look beyond the personal suffering of patients and relatives, the cost of an infectious complication is estimated at around €10,000 per patient. Hence, total cost in only the Netherlands alone can be calculated at up to €3.5B per year, as approximately 350,000 Dutch patients go through an infection after surgery yearly. Total EU costs are even more shocking, at an estimated €125B per year.
Once our algorithm moves beyond 90% accuracy and is fully certified we expect to make a dent in this leading to advantages for everybody: reduction in personal suffering, reduction in overhead, reduction in bedding, reduction in staff needed, reduction in costs. Time will prove what reduction we can achieve but given the staggering absolute numbers we expect this to be significant.
Some 50M people need to undergo inpatient surgery per year in Europe. Yet, despite intensive research efforts, around 25% of surgical patients will experience a complicated recovery with some type of infection on their path: pneumonia, urinary tract infections, wound infections, abdominal infections and bacteremia. Of course, the risk varies depending on patient and procedure, but despite efforts like biomarkers, risk scores or devices for early warning/detection, and preventive methods like ‘Enhanced Recovery After Surgery’ (ERAS), the overall risk still leaves almost one in four patients to get an infection within 30-days after surgery.
If we look beyond the personal suffering of patients and relatives, the cost of an infectious complication is estimated at around €10,000 per patient. Hence, total cost in only the Netherlands alone can be calculated at up to €3.5B per year, as approximately 350,000 Dutch patients go through an infection after surgery yearly. Total EU costs are even more shocking, at an estimated €125B per year.
Once our algorithm moves beyond 90% accuracy and is fully certified we expect to make a dent in this leading to advantages for everybody: reduction in personal suffering, reduction in overhead, reduction in bedding, reduction in staff needed, reduction in costs. Time will prove what reduction we can achieve but given the staggering absolute numbers we expect this to be significant.