scholarly journals Data Mining Session-Based Patient Reported Outcomes (PROs) in a Mental Health Setting: Toward Data-Driven Clinical Decision Support and Personalized Treatment

2011 ◽  
Author(s):  
Casey Bennett ◽  
Thomas Doub ◽  
April Bragg ◽  
Jason Luellen ◽  
Christina Van Regenmorter ◽  
...  
Keyword(s):  
Mental Health ◽  
Data Mining ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Patient Reported Outcomes ◽  
Clinical Decision ◽  
Data Driven ◽  
Personalized Treatment ◽  
Mental Health Setting ◽  
Patient Reported

Digitale Medizin in der Onkologie: Clinical Decision Support, Real World Data und Patient Involvement

2019 ◽  
Vol 144 (07) ◽  
pp. 430-434 ◽  
Author(s):  
Sebastian Wagner ◽  
Hubert Serve
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Real World ◽  
Patient Reported Outcomes ◽  
Patient Involvement ◽  
Clinical Decision ◽  
Real World Data ◽  
World Data ◽  
Diagnostik Und Therapie ◽  
Patient Reported

Was ist neu? Stand der Dinge In der Onkologie kam es zuletzt zu einem rasanten Wissenszuwachs. Neue Entwicklungen in Diagnostik und Therapie von Tumorerkrankungen haben die Grundlagen für individualisierte Therapiekonzepte geschaffen. Aktuell werden innovative Onkologie-spezifische IT-Lösungen entwickelt mit dem Ziel, die Heilungschancen für Patienten mit Tumorerkrankungen langfristig zu verbessern. Clinical Decision Support Die Komplexität onkologischer Therapieentscheidungen hat durch Einführung neuer Biomarker und zielgerichteter Therapeutika stark zugenommen. Erste „intelligente“ Systeme, die aktiv Therapieoptionen auf Basis von vorhandenen Daten vorschlagen, sind verfügbar, aber noch nicht weit verbreitet und unzureichend klinisch validiert. Real-World Data und Real-World Evidence Durch die zunehmende Verbreitung von elektronischen Gesundheitsakten wird eine strukturierte Sammlung und Auswertung von Daten aus der onkologischen Routineversorgung möglich. Real World Data werden eingesetzt, um die Sicherheit und Nebenwirkungen von onkologischen Medikamenten zu überwachen und können helfen, onkologische Therapieleitlinien zu entwickeln. Patient Involvement und Patient Reported Outcomes Die frühe Meldung von Symptomen und Nebenwirkungen (Patient Reported Outcomes) verspricht eine verbesserte Behandlung und eine gesteigerte Therapieadhärenz. Patient Reported Outcomes können auch im Rahmen von klinischen Studien und zur Qualitätssicherung eingesetzt werden. Erste Studien zeigen, dass eine IT-gestützte Erfassung von Patient Reported Outcomes Symptome und Überleben von Patienten mit Tumorerkrankungen positiv beeinflussen kann.

scholarly journals Utility of a Mobile Clinical Decision Support Application in Patients Undergoing Spinal Cord Stimulation.

Neurosurgery ◽  
2019 ◽  
Vol 66 (Supplement_1) ◽  
Author(s):  
Shervin Rahimpour ◽  
Sarah E Hodges ◽  
Luis A Antezana ◽  
Abena A Ansah-Yeboah ◽  
Rajeev Dharmapurikar ◽  
...  
Keyword(s):  
Spinal Cord ◽  
Chronic Pain ◽  
Decision Support ◽  
Patient Reported Outcomes ◽  
Clinical Decision ◽  
Software Platform ◽  
Clinical Specialist ◽  
Mobile Software ◽  
Patient Reported

Abstract INTRODUCTION Novel technologies to support real-time patient education, engagement and scalable outcomes monitoring to make clinically meaningful decisions are needed. The ManageMySurgery (MMS) Spinal Cord Stimulation (SCS) module is a mobile clinical decision support application that provides: (1) a mobile, patient-centered engagement tool for delivering pre-, peri- and postoperative SCS information; (2) scalable patient-reported outcomes collection; (3) a HIPAA-compliant 2-way messaging platform with a Clinical Specialist Educator for real-time support and goal setting. METHODS Prospective data was collected using the MMS mobile smartphone application in patients undergoing Medtronic SCS trial and permanent implant procedures. E-consent was obtained through the HIPAA compliant, mobile software platform. All data was de-identified, aggregated and analyzed. RESULTS A total of 20 patients (15-trial SCS and 5 permanent SCS patients) agreed to participate and logged onto the mobile software platform. For trial SCS patients, 100% of those that participated experienced >50% pain relief as documented in their patient-reported outcomes. Furthermore, patients found various features of the software platform helpful for navigating different aspects of their SCS procedure, with 81% finding MMS helpful in preparing for their SCS procedure, 88% finding MMS helpful in recovering from their SCS procedure and 94% in communicating with their Clinical Specialist Educator. In addition, 95% of patients would recommend MMS to a friend or family member. CONCLUSION The MMS platform appears to have utility both during the SCS Trial and Permanent procedures. In patients with chronic pain, novel patient engagement and follow-up tools such as MMS may be a good option for keeping patients engaged with the therapy and ensuring patients stay on track during their procedural journey. Randomized, controlled trials with extended follow-up are in progress and needed to further evaluate the utility of MMS in patients with chronic pain undergoing SCS.

Mental Health Clinical Decision Support Exploiting Big Data

2019 ◽  
pp. 160-184 ◽  
Author(s):  
Jan Kalina
Keyword(s):  
Mental Health ◽  
Decision Making ◽  
Big Data ◽  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Clinical Decision Making ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision

The complexity of clinical decision-making is immensely increasing with the advent of big data with a clinical relevance. Clinical decision systems represent useful e-health tools applicable to various tasks within the clinical decision-making process. This chapter is devoted to basic principles of clinical decision support systems and their benefits for healthcare and patient safety. Big data is crucial input for clinical decision support systems and is helpful in the task to find the diagnosis, prognosis, and therapy. Statistical challenges of analyzing big data in psychiatry are overviewed, with a particular interest for psychiatry. Various barriers preventing telemedicine tools from expanding to the field of mental health are discussed. The development of decision support systems is claimed here to play a key role in the development of information-based medicine, particularly in psychiatry. Information technology will be ultimately able to combine various information sources including big data to present and enforce a holistic information-based approach to psychiatric care.

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