Int J Clin Pharm DOI 10.1007/s11096-013-9884-7
LETTER TO THE EDITOR
A fully automated medication review? Hugo A. J. M. de Wit • Carlota Mestres Gonzalvo Rob Janknegt • Jos M. G. A. Schols • Paul-Hugo M. van der Kuy
•
Received: 7 November 2013 / Accepted: 11 November 2013 Ó Koninklijke Nederlandse Maatschappij ter bevordering der Pharmacie 2013
In a letter to the editor concerning the manuscript ‘‘Development of a computer system to support medication reviews in nursing homes’’ the authors state that the proposed computer system to support medication reviews is a good concept but not new [1]. Thank you for your reaction to our manuscript. We agree that the concept of a clinical decision support system (CDSS) is not new nor the idea to use these techniques to support medication reviews. Furthermore, the concept of Multiple Classification Ripple-Down Rules (MCRDR) methodology appears to use state of the art programming. However, we have different approach since our system is able to support the medication review fully automated. All patients’ data used in our CDSS are retrieved and processed electronically. The data concerning medication, laboratory results and diagnosis are extracted from different systems and loaded into the CDSS. Bindoff et al. describe a part of their software element to be a standard database ‘‘front-end’’ where the user can manually enter a single patient’s details such as medical history, medication and laboratory data. In this system the average time needed for analysing each patient is 9 min [2]. Currently, we analyse 2,000 patients daily by automation. Entering
H. A. J. M. de Wit (&) C. Mestres Gonzalvo R. Janknegt P.-H. M. van der Kuy Department of Clinical Pharmacy and Toxicology, Orbis Medical Centre, Dr. H van der Hoffplein 1, 6162 BG Sittard-Geleen, The Netherlands e-mail: [email protected] J. M. G. A. Schols Department of General Practice and Department of Health Services Research, CAPHRI-School for Public Health and Primary Care, Maastricht University, Maastricht, The Netherlands
patients details to any system will be very time consuming and sensitive to errors, whereas time needed for data gathering is not even considered. Furthermore, the authors suggest that the development of evidence-based clinical rules is unwise because of difficulties in validation and maintenance. We believe that evidence-based medicine is the best way to develop clinical rules. The opinion of a single pharmacist or physician to create a clinical rule can be helpful but is not necessarily correct. Therefore, we have chosen to maintain and develop our clinical rules based on the advice of an expert panel provided by feedback from physicians and pharmacists whom perform medication reviews regularly, new guidelines and literature. In our experience many clinical rules suggested by physicians and pharmacist need revision after consideration by the expert panel. This method of developing and optimising clinical rules results in maximal sensitivity and specificity. For instance, our system only alerts when the dosage of a specific drug should be adjusted based on the kidney function (see Fig. 1). This is done by using cut-off points depending on the dose and the renal function values for all drugs that are influenced by renal function. The alerts therefore have a maximum positive predictive value. In contrast, the demosystem shown on www.medscope.com.au merely alerts that drugs should be checked manually for dosages based on the manually entered decreased renal function. The highly individualised method used in MCRDR can be a solution when pharmacists use the system as a ‘‘standalone’’. However, when used by different pharmacists in one database as an ‘‘enterprise solution’’, the pharmacists can influence the alerts of other pharmacists by creating new clinical rules or changing them without any control of an expert-panel or evidence-based information. For instance, we are often debating the relevant cut-off
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Int J Clin Pharm
Fig. 1 An example of an algorithm based clinical rule combining laboratorium and pharmacy data
point of a hyperkalemia clinical rule. The clinical relevance of hyperkalemia is interpreted differently for a patient of a general practitioner than of a nephrologist. Designing a good clinical rule with an optimal cut-off point is difficult and this can be achieved differentiate the clinical rules on the patient characteristics, medication and laboratory data and not on the pharmacist or physician. In addition, we believe some clinical rules cannot be developed by artificial intelligence (yet). We are developing clinical rules able to predict deliriums and falls by weighing many different risk factors. On the other hand, our system does not incorporate adverse drug events yet. The adjustment of medication based on patients’ feedback is currently under construction. For this matter we are developing a system to electronically register adverse events. We believe our system has a different approach since we are developing a fully automated engine without having to
123
manually enter patients’ data which can continuously screen patients. Furthermore, we are developing ‘evidencebased’ clinical rules which will be implemented in hospital and in nursing home settings. Nevertheless, we are interested in the ‘‘knowledge acquisition’’ approach and we believe that exchanging expertise can support this research field even further.
References 1. de Wit HA, Mestres Gonzalvo C, et al. Development of a computer system to support medication reviews in nursing homes. Int J Clin Pharm. 2013;35(5):668–72. 2. Bindoff IK, Tenni PC, et al. Development of an intelligent decision support system for medication review. J Clin Pharm Ther. 2007;32(1):81–8.
LETTER TO THE EDITOR
A fully automated medication review? Hugo A. J. M. de Wit • Carlota Mestres Gonzalvo Rob Janknegt • Jos M. G. A. Schols • Paul-Hugo M. van der Kuy
•
Received: 7 November 2013 / Accepted: 11 November 2013 Ó Koninklijke Nederlandse Maatschappij ter bevordering der Pharmacie 2013
In a letter to the editor concerning the manuscript ‘‘Development of a computer system to support medication reviews in nursing homes’’ the authors state that the proposed computer system to support medication reviews is a good concept but not new [1]. Thank you for your reaction to our manuscript. We agree that the concept of a clinical decision support system (CDSS) is not new nor the idea to use these techniques to support medication reviews. Furthermore, the concept of Multiple Classification Ripple-Down Rules (MCRDR) methodology appears to use state of the art programming. However, we have different approach since our system is able to support the medication review fully automated. All patients’ data used in our CDSS are retrieved and processed electronically. The data concerning medication, laboratory results and diagnosis are extracted from different systems and loaded into the CDSS. Bindoff et al. describe a part of their software element to be a standard database ‘‘front-end’’ where the user can manually enter a single patient’s details such as medical history, medication and laboratory data. In this system the average time needed for analysing each patient is 9 min [2]. Currently, we analyse 2,000 patients daily by automation. Entering
H. A. J. M. de Wit (&) C. Mestres Gonzalvo R. Janknegt P.-H. M. van der Kuy Department of Clinical Pharmacy and Toxicology, Orbis Medical Centre, Dr. H van der Hoffplein 1, 6162 BG Sittard-Geleen, The Netherlands e-mail: [email protected] J. M. G. A. Schols Department of General Practice and Department of Health Services Research, CAPHRI-School for Public Health and Primary Care, Maastricht University, Maastricht, The Netherlands
patients details to any system will be very time consuming and sensitive to errors, whereas time needed for data gathering is not even considered. Furthermore, the authors suggest that the development of evidence-based clinical rules is unwise because of difficulties in validation and maintenance. We believe that evidence-based medicine is the best way to develop clinical rules. The opinion of a single pharmacist or physician to create a clinical rule can be helpful but is not necessarily correct. Therefore, we have chosen to maintain and develop our clinical rules based on the advice of an expert panel provided by feedback from physicians and pharmacists whom perform medication reviews regularly, new guidelines and literature. In our experience many clinical rules suggested by physicians and pharmacist need revision after consideration by the expert panel. This method of developing and optimising clinical rules results in maximal sensitivity and specificity. For instance, our system only alerts when the dosage of a specific drug should be adjusted based on the kidney function (see Fig. 1). This is done by using cut-off points depending on the dose and the renal function values for all drugs that are influenced by renal function. The alerts therefore have a maximum positive predictive value. In contrast, the demosystem shown on www.medscope.com.au merely alerts that drugs should be checked manually for dosages based on the manually entered decreased renal function. The highly individualised method used in MCRDR can be a solution when pharmacists use the system as a ‘‘standalone’’. However, when used by different pharmacists in one database as an ‘‘enterprise solution’’, the pharmacists can influence the alerts of other pharmacists by creating new clinical rules or changing them without any control of an expert-panel or evidence-based information. For instance, we are often debating the relevant cut-off
123
Int J Clin Pharm
Fig. 1 An example of an algorithm based clinical rule combining laboratorium and pharmacy data
point of a hyperkalemia clinical rule. The clinical relevance of hyperkalemia is interpreted differently for a patient of a general practitioner than of a nephrologist. Designing a good clinical rule with an optimal cut-off point is difficult and this can be achieved differentiate the clinical rules on the patient characteristics, medication and laboratory data and not on the pharmacist or physician. In addition, we believe some clinical rules cannot be developed by artificial intelligence (yet). We are developing clinical rules able to predict deliriums and falls by weighing many different risk factors. On the other hand, our system does not incorporate adverse drug events yet. The adjustment of medication based on patients’ feedback is currently under construction. For this matter we are developing a system to electronically register adverse events. We believe our system has a different approach since we are developing a fully automated engine without having to
123
manually enter patients’ data which can continuously screen patients. Furthermore, we are developing ‘evidencebased’ clinical rules which will be implemented in hospital and in nursing home settings. Nevertheless, we are interested in the ‘‘knowledge acquisition’’ approach and we believe that exchanging expertise can support this research field even further.
References 1. de Wit HA, Mestres Gonzalvo C, et al. Development of a computer system to support medication reviews in nursing homes. Int J Clin Pharm. 2013;35(5):668–72. 2. Bindoff IK, Tenni PC, et al. Development of an intelligent decision support system for medication review. J Clin Pharm Ther. 2007;32(1):81–8.
