Clinical Decision Support Systems (CDSS) for preventive management of COPD patients
This is review of an article about Clinical decision system CDS by Velickovski, F, Ceccaroni,L , Roca, J, Burgos, F, Galdiz, J, Marina, M, Lluch-Ariet, M. (2014). Clinical Decision Support Systems (CDSS) for preventive management of COPD patients.
According to Velickovski et al, CDS facilitates the adherence to best-practice medicine, especially during important decision making over a patient care. Therefore, the objective of this article was to design, develop and assess a CDS that enhance early detection and assessment of Chronic obstructive pulmonary disease (COPD). The article reported, the system was developed following cycles of: requirement-adjustment, development and validation. As a result a CDS that supports COPD early detection and diagnosis, Spirometry quality control and patient stratification was developed and integrated to the existing health information system. Finally the article suggested specialized CDS can be integrated to the existing electronic healthcare system to refine diagnosis and assessment of COPD.
The article noted that CDS can narrow the gap between what is acceptable as an optimal evidence-based medical practice and the actual practice that is being delivered to patients. Consequently, the article reviewed the development of a CDS that provides fast, reliable and directly applicable advice when making decisions about care of a patient with Chronic obstructive pulmonary disease (COPD). The CDS also addressed the heterogeneity of the disease by creating a categorization based other clinical standards.
The article enumerates characteristics of successful CDS based on based on previous related works as follows: • A successful CDS integrates into an existing work-flow • It is available at the time and place of decision making • It suggests actionable recommendation’s
According to Velickovski et al, before the development of the CDS starts different features of it such as: Architecture, Controller, Reasoning engine and Clinical knowledge base, Quality control module, Reference value module, External supporting synergy-COPD system and Clinical data representation were articulated and selected based up on the need at hand. Then the CDS system was developed in an incremental cycle format, where the first phase included: identifying requirements, designing, development and validation and testing being the last step of the first cycle. The second phase, which is relatively a constant cycle, consists: requirement adjustment, knowledge acquisition, knowledge engineering, validation and testing followed by deployment, which again led to a new cycle of all the above steps.
Further the article stated, after designing and implementing the CDS was deployed to a secure online platform, which is connected to an existing health information system (HIS) or EMR. Some of the components of the deployed CDS are as follows: • Spirometery quality control: determines the optimal functioning of different parameters of the spirometer. • Case finding: Eligibility for spirometery test: this component generates patient specific advice for further spirometery testing based on inclusion/exclusion criteria saved in the clinical knowledge-base. • Case finding: Preliminary evaluation: based on pre-bronchodilation spirometery result this system recommends whether or not a subject needs to seek a primary healthcare service for further investigation. • Diagnosis: Primary care evaluation: this system decides if a probable COPD case exists based on pre and post bronchodilation spirometery test and warrants advice for the patient. • Assessment: Patient stratification: this system categorizes patients into different categories of the heterogeneous disease profile based on post-bronchodilation spirometery result and COPD assessment test. In addition to that, based on given stratification patient would have a specific treatment recommendation.
Evaluation of the CDS as a diagnosis service.
The validation of CDS was done on 323 cases. The performance of the system was compared to a de-identified database of patients from primary care centers. Out of the 323 cases the system diagnosed 101 cases as likely COPD and 222 unlikely COPD. The article reported that 92% CDS diagnosis matched the assessment of specialists in the field. Aside form that, the article also added the integration of the system to an existing HIS was seamless having a response time within seconds.
• Instead of using experts panel only one respiratory expert was used • The use of CDS was dictated by the current policy and procedure for the treatment of COPD • The CDS was under evaluation protocol instead of directly applied to actual patients
The study concluded that specialized CDS could be integrated to the existing system, which leads to a more accurate and timely diagnosis and management of COPD cases. The study also demonstrate the system can be integrated with the existing system without causing any workflow inconvenient.
This article showed how specialized CDS can help in refining accurate and timely diagnosis of COPD. The development of this CDS addresses the challenge encounter by providers due to the heterogeneous nature of the disease. It also showed that solutions like this, has a potential to narrow down the gap between what is assumed as optimal standard of evidence based medicine and the actual care that is being delivered. Further, it was demonstrated that system like this can be integrated within an existing HIS. Finally, it would be beneficial to apply the results of the study to actual patient care beyond COPD.