Introduction: Macro-data informing business-industry has become commonplace and it is suggested that it can be equally useful for healthcare-industry giving clinicians and healthcare managers powerful information on population health and service performance.
Objective: To build electronic surveillance system that allows Defence Forces Physiotherapy (DF Physio) to clinically audit 'live' data, to inform on service performance, planning strategy and population health.
Methods: A detailed surveillance system was built by Defence Forces Lead Physiotherapist within the electronic healthcare record system, Socrates©, based on international guideline documents [1,2, 3, 4, 5, 6]. At Year One, there are approximately 450,000 data-points based on 9,000 patient contacts using multiple outcome measures (200+), such as Numerical Pain Rating Scale (NPRS), Patient Specific Functional Scale (PSFS) and number of appointments. This anonymised data was interrogated using correlation matrix, association rules and conditional inference tree, to investigate DF Physio effectiveness in treating its population, and as an example of how the surveillance system can work to inform service. (285 observations, 4 variables/entry = 1,140 data-points).
Results: Statistically significant linear relationships between median-NPRS, median-PSFS and number of appointments were recorded at Clinical Discharge using an F-test:
37% of patients achieved a Function Gain (+ 1) and Pain Loss (- 3),
50% of patients achieved a Function Gain (+2/+3) and Pain Loss (-4),
12% of patients achieved a Function Gain (+4) and Pain Loss (- 5).
Discussion This example of practice demonstrates the use of clinically auditing ‘live’ macro-data, to inform on service performance, planning strategy and population health; and that DF Physio is effective in treating its population.
Implications for this type of system would be the theoretical applicability to wide-ranging healthcare settings such as national hospitals in order to better inform strategy, planning, performance and improve patient healthcare outcomes.
Areas for future systems and technology development would be population of this system by voice command using natural language processing technology to increase efficiency and the integration of clinical test sensitivity and specificity in order to allow artificial intelligence machine-learning to aid clinician decisions, consider Apple Glass© / IBM Watson interaction.
References:
[1] OSICS, Orchard Coding System - Dr. John Orchard.
[2] Linda T. Kohn, Janet M. Corrigan, and Molla S. Donaldson. To Err is Human: Building a Safer Health System. National Academies Press, 2000.
[3] Committee on Quality of Health Care in America. Crossing the Quality Chasm: A New Health System for the 21st Century. National Academies Press, 2001.
[4] HIQUA. National Standards for Better, Safer Healthcare. 2012.
[5] WCPT. Standards and Guidelines on Records Management. www.wcpt.org.
[6] Quality and Patient Safety Directorate. A Practical Guide to Clinical Audit. Health Service Executive, (2017).
Musculoskeletal , Service Development , Other
