scholarly journals Primary Sense: a new population health management tool for general practice

2020 ◽  
Vol 26 (3) ◽  
pp. 212
Author(s):  
Deborah Davies

Primary Sense is a new data extraction, analysis and reporting tool that the Gold Coast Primary Health Network (GCPHN) has developed to enable practical and effective population health management in general practice and also on a regional level. Once installed, the tool de-identifies data within the practice before running it through various clinical risk algorithms to create practical information that can easily be actioned within the general practice business model in at least two ways. The first is to generate up-to-date reports of patients who are most likely to benefit from specific interventions or occasions of service. The second is to identify potentially serious medication safety issues, alerting clinicians in real time at point of prescribing. Formal live testing of the system was completed in nine practices where 22 managers and nurses and 42 GPs used the tool over a 5-month period in 2019. The live test monitored the use of reports and alerts, and regular feedback from users enabled small but important improvements to the tool. Practice teams successfully used the reports to target specific groups of patients with outstanding care needs or who were at greatest risk of adverse health outcomes. The results of the live test showed that users found Primary Sense to be easy to use and beneficial to general practice. The next phase of this project is now underway to further trial the scalability and change management requirements for full implementation of Primary Sense. As more and more practices adopt the tool, the aggregated data will increasingly help to support population health planning, commissioning of local services, active health surveillance and other related activities.

Iproceedings ◽  
2016 ◽  
Vol 2 (1) ◽  
pp. e17
Author(s):  
Sashi Padarthy ◽  
Cristina Crespo ◽  
Keri Rich ◽  
Nagaraja Srivatsan

Computers ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 4
Author(s):  
Silvia Panicacci ◽  
Massimiliano Donati ◽  
Francesco Profili ◽  
Paolo Francesconi ◽  
Luca Fanucci

Together with population ageing, the number of people suffering from multimorbidity is increasing, up to more than half of the population by 2035. This part of the population is composed by the highest-risk patients, who are, at the same time, the major users of the healthcare systems. The early identification of this sub-population can really help to improve people’s quality of life and reduce healthcare costs. In this paper, we describe a population health management tool based on state-of-the-art intelligent algorithms, starting from administrative and socio-economic data, for the early identification of high-risk patients. The study refers to the population of the Local Health Unit of Central Tuscany in 2015, which amounts to 1,670,129 residents. After a trade-off on machine learning models and on input data, Random Forest applied to 1-year of historical data achieves the best results, outperforming state-of-the-art models. The most important variables for this model, in terms of mean minimal depth, accuracy decrease and Gini decrease, result to be age and some group of drugs, such as high-ceiling diuretics. Thanks to the low inference time and reduced memory usage, the resulting model allows for real-time risk prediction updates whenever new data become available, giving General Practitioners the possibility to early adopt personalised medicine.


2019 ◽  
Vol 156 (6) ◽  
pp. S-1275-S-1276
Author(s):  
David A. Jacob ◽  
Vera Yakovchenko ◽  
Linda Chia ◽  
Andrew Himsel ◽  
Diana Ruiz ◽  
...  

2014 ◽  
Author(s):  
Sarah Klein Klein ◽  
Douglas McCarthy McCarthy ◽  
Alexander Cohen Cohen

PM&R ◽  
2017 ◽  
Vol 9 ◽  
pp. S75-S84 ◽  
Author(s):  
Todd Rowland ◽  
Jill Nielsen-Farrell ◽  
Kathy Church ◽  
Barbara Riddell

Sign in / Sign up

Export Citation Format

Share Document