Mendolor delivers pain management system for post-operative pain management in hospital and other health care provider environments. Pain management consists of a tutorial, pain measurement procedure and data collection. It is based on machine learning, which integrates subjective pain to objective biometrics and thus, is able to learn how certain kind of pain will develop after the surgery.
(Mendolor uses Kipuwex censors).

Problem:
Without accurate knowledge of patients' pain, the treatments cannot be accurate.

Solution:
Mendolor uses Kipuwex sensors that are able to measure various biometrics such as HR, HRV, NFSC, PPG etc. With combination of neural composite algorithm integrated with patient data, medication and their subjective estimation of pain (McGill vocabulary) an accurate prediction of the development of pain is possible.

In hospital the device reminds and alerts patients to give estimation of their current pain level for the doctor the time they have been asked to do ...
Mendolor delivers pain management system for post-operative pain management in hospital and other health care provider environments. Pain management consists of a tutorial, pain measurement procedure and data collection. It is based on machine learning, which integrates subjective pain to objective biometrics and thus, is able to learn how certain kind of pain will develop after the surgery.
(Mendolor uses Kipuwex censors).

Problem:
Without accurate knowledge of patients' pain, the treatments cannot be accurate.

Solution:
Mendolor uses Kipuwex sensors that are able to measure various biometrics such as HR, HRV, NFSC, PPG etc. With combination of neural composite algorithm integrated with patient data, medication and their subjective estimation of pain (McGill vocabulary) an accurate prediction of the development of pain is possible.

In hospital the device reminds and alerts patients to give estimation of their current pain level for the doctor the time they have been asked to do so (normally six times a day). However, if patients’ conditions change notably, they are able to send their own valuations, whenever they feel there is need to do that. In this case, the system alerts the doctor and nurses, who are able to look, if the change in pain intensity levels is significant and, if there is need for changes in medication or other procedures.

All alerts and followed actions are scribed in a system, where data can be analyzed and compared to other patient records, thus increasing knowledge of different pain treatments.

The data for research purposes is separate from personal data kept by the hospital. The data is collected for the purpose of finding new knowledge of patients individual differences, medication and the treatments of different types of pain.

The value offer is to help doctors and nurses in identifying the changes of pain leves for better treatment and to help patients to recover from postoperative pain both in-hospital and, afterwards that, at home.

For research application opens new possibilities.
More information

Employees

Lauri Kotaja
Admin

Recommendations

Pirjo Koivukangas

The measurement method in question is an exceptional addition to the suite of metrics currently in use in pain measurement and quality of life indices in the healthcare sector. The novelty of a proven pain index (the VAS in use here) in a mobile or digital interface allows for a level of control and interactivity within a healthcare service provider’s ecosystem not yet realized in the healthcare sector.

This interfacing allows also for management and documentation of pain over a longer period of time, which in turn is useful in follow-ups and the identifying of the exact nature of pain in question, possible treatment options and their efficiency over time.
I have every expectation that the team assembled for this project will succeed in their efforts.

Pirjo Koivukangas
Ph.D (Economics) Docent in Health Economics