Scheduling of Remote Monitoring for Peritoneal Dialysis Patients.
Maria Grazia PetrisNiccolò MorisiSabrina Milan MananiIlaria TantilloJosé David Gonzàlez BarajasBladimir Diaz VillavicencioClaudia CastiglioneGaetano AlfanoGabriele DonatiMonica ZanellaPublished in: Journal of clinical medicine (2024)
Peritoneal dialysis (PD) is performed as a home-based treatment and in this context, telemedicine has been proven helpful for improving clinicians' surveillance and maintaining PD patients in their home setting. The new e-health devices make remote patient monitoring (RPM) for automated peritoneal dialysis (APD) treatment possible, evaluating the data at the end of every treatment and adapting the prescription at distance if necessary. This paper aims to share a method for improving clinical surveillance and enabling PD patients to receive their treatment at home. In the present case series, we delineate the clinical protocol of the Vicenza PD Center regarding patient characteristics, timing, and the purpose of the APD-RPM. We present the Vicenza PD Center's experience, illustrating its application through three case reports as exemplars. Telemedicine helps to carefully allocate healthcare resources while removing the barriers to accessing care. However, there is a risk of data overload, as some data might not be analyzed because of an increased workload for healthcare professionals. A proactive physician's attitude towards the e-health system has to be supported by clinical instructions and legislative rules. International and national guidelines may suggest which patients should be candidates for RPM, which parameters should be monitored, and with what timing. According to our experience, we suggest that the care team should define a workflow that helps in formulating a correct approach to RPM, adequately utilizing resources. The workflow has to consider the different needs of patients, in order to assure frequent remote control for incident or unstable patients, while prevalent and stable patients can perform their home treatment more independently, helped by periodic and deferred clinical supervision.
Keyphrases
- end stage renal disease
- peritoneal dialysis
- chronic kidney disease
- healthcare
- newly diagnosed
- ejection fraction
- type diabetes
- public health
- palliative care
- emergency department
- cardiovascular disease
- randomized controlled trial
- machine learning
- primary care
- risk assessment
- quality improvement
- mental health
- high throughput
- deep learning
- chronic pain
- clinical practice
- patient reported outcomes
- big data
- artificial intelligence
- social media