ARTÍCULO ESPECIAL

 

The role of the Pharmacist in the design, development and implementation of Medication Prescription Support Systems

Papel del farmacéutico en el diseño, desarrollo e implantación de sistemas de soporte a la prescripción de medicamentos

 

 

Núria Solà Bonada¹, Ana María Álvarez Díaz², Carlos Codina Jané³ and TECNO Work Group of the SEFH⁴

¹ Pharmacy Unit. Hospital Universitari de Vic. Barcelona,
² Pharmacy Unit. Hospital Universitario Ramón y Cajal, Madrid.
³ Pharmacy Unit. Hospital Clinic, Barcelona.
⁴ Teresa Bermejo Vicedo (Coordinator). Hospital Universitario Ramón y Cajal, Madrid. Ana Maria Álvarez Diaz. Hospital Universitario Ramón y Cajal, Madrid. Carlos Codina Jané. Hospital Clinic, Barcelona. Carmen Encinas Barrios. Hospital General de Ciudad Real. Maria Queralt Gorgas. Corporació Sanitària Parc Tauli, Sabadell. Ana Herranz Alonso. Hospital General Universitario Gregorio Marañón, Madrid. Amelia de La Rubia Nieto. Hospital Universitario Virgen de la Arrixaca, Murcia. Isabel Martin Herranz. Complejo Hospitalario Juan Canalejo, La Coruña. Eva Negro Vega. Hospital Universitario de Getafe. Moisés Pérez León. Hospital Universitario de Gran Canaria Doctor Negrin. Laura Domenech Moral. Hospital Vall d'Hebron. Elena Lobato Matilla. Hospital General Universitario Gregorio Marañón. Spain.

Correspondence

 

 

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ABSTRACT

Clinical Decision Support Systems (CDSS) are computerized tools designed to help healthcare professionals to make clinical and therapeutic decisions, with the objective of improving patient care. Prescription-targeted CDSS have the highest impact in improving patient safety. Although there are different designs and functionalities, all these systems will combine clinical knowledge and patient information in a smart manner, in order to improve the prescription process. With the emergence of new technologies and advances in smart decision systems, the implementation of said systems can achieve an important improvement in terms of the prescription process and patient safety.
The design and implementation of these systems should be performed by a multidisciplinary team of professionals, where Pharmacists will play an important role due to their technical knowledge about medications and the technologies associated to their use.
This article aims to provide basic guidelines for the design and adequate implementation, monitoring and follow-up of Clinical Decision Support Systems within the setting of pharmacological prescription.

Key words: Decision Support Systems; Prescription; Medication alert systems.

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RESUMEN

Los sistemas de soporte a la decisión clínica son herramientas informáticas diseñadas para ayudar a los profesionales sanitarios en la toma de decisiones clínicas y terapéuticas, con la finalidad de mejorar la atención a los pacientes. Los sistemas dirigidos a la prescripción son los que mayor impacto tienen en la mejora de la seguridad de los pacientes. Aunque existen diferentes diseños y funcionalidades, estos sistemas coinciden en combinar de manera inteligente el conocimiento clínico y la información de los pacientes, para mejorar el proceso de prescripción. Con la aparición de las nuevas tecnologías y el avance de los sistemas inteligentes de decisión, la implantación de estos sistemas puede lograr una mejora importante en el proceso de prescripción y en la seguridad de los pacientes.
El diseño e implantación de estos sistemas se debe llevar a cabo por un equipo multidisciplinar de profesionales, donde el farmacéutico tiene un papel destacado gracias a sus conocimientos técnicos sobre los medicamentos y sobre las tecnologías asociadas a la utilización de los mismos.
Con este artículo se pretende aportar las directrices básicas para el diseño y la correcta implantación, monitorización y seguimientos de los Sistemas de Soporte a la Decisión Clínica en el ámbito de la prescripción farmacológica.

Palabras clave: Sistemas de soporte a la decisión; Prescripción; Sistemas de alerta de medicación.

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Introduction

During the last decades, there have been major advances in the development of technologies applied to the healthcare setting. Different international and national organizations, such as the Institute of Medicine¹, Joint Commission on Accreditation of Healthcare Organizations², National Patient Safety Foundation³, American Medical Informatics Association⁴, National Quality Forum⁵, Healthcare Information and Management Systems Society⁶ or the Spanish Ministry of Health and Social Policy⁷, are advocating for the use of information systems by healthcare organizations, in order to improve quality, safety and cost-efficacy in patient care.

Assisted Electronic Prescription (AEP), Automated Medication Dispensing Systems (AMDS), Electronic Clinical Records (ECRs), and electronic records for medication administration, are clear examples of the way in which technologies are gradually being incorporated into the patient care process, increasing the quality, safety and efficacy of each stage^8,9. Fifty per cent (50%) of adverse events associated with medication errors can be prevented, and the implementation of these technologies can help to prevent them^10-12. In Spain, the ENEAS study demonstrated that 42.8% of adverse events could be prevented, and that over one third of these adverse events were medication-related¹³.

Many ECR and AEP systems are also incorporating Clinical Decision Support Systems (CDSS)¹⁴. These CDSS are computerized tools designed in order to help healthcare professionals at the time of making clinical and therapeutical decisions, with the aim to improve patient care. These can be incorporated at any stage in the healthcare process, such as diagnosis, treatment or prevention, as well as by any professional in the healthcare team. However, the area of medication use is where there is more experience, and specifically, in the prescription stage.

The objective of this article is to describe the characteristics of CDSS and the recommendations for their design, implementation, and monitoring.

 

Definition and characteristics of CDSS

CDSS are a set of computerized tools that integrate information on medications and data associated with the clinical processes of patients^15,16. These systems combine clinical knowledge and patient information in a smart manner, presenting recommendations to the relevant professional at the adequate time, and offering alternative options specific for the characteristics of each patient¹⁷.

The design of these systems is based on the combination of the clinical data included in patient ECRs, with the information available about the use of a medication. Through decision criteria, structured within the computerized system, alert processes are activated in those cases where the use of a medication requires a specific action, according to the clinical characteristics of the patient. These systems can address all the stages in the process of medication use: prescription, validation, administration, follow-up, etc., though they will be more effective in initial stages, such as prescription¹¹.

There are different ways to classify CDSS (Table 1): Based on configuration, design and/or functioning, prescription support systems can be classified into passive or active¹⁸. Passive systems have voluntary selection, and the user must select proactively to visualize the information in the system; while in active systems, an alert is automatically triggered by specific conditions. The latter have demonstrated a higher impact on detecting medication-related problems¹⁹. On the other hand, these alert processes can be of informative nature only or promote the interruption of the action. In the latter case, they are programmed to interrupt the process under development, allowing the physician to complete or not the prescription, based on the level of importance of the recommendation. Kupperman GJ et al. defined two types of CDSS r: Basic CDSS, generating alerts about allergies, drug interactions, maximum or minimum doses, or pharmacological duplication, and advanced CDSS, containing patient data (lab test parameters, clinical status, etc.), structured clinical practice guidelines and protocols, allowing drug selection and dosing according to comorbidities. These systems intend to facilitate the conciliation between healthcare levels, the automatic detection of adverse events based on symptoms, lab test data, diagnostic outcomes, or notes about the patient^20,21. Advanced systems issue a higher proportion of clinically relevant alerts than Basic systems²².

 

 

Impact of CDSS on patient safety

The possibility of integrating clinical practice guidelines, alerts that state dosing changes, or suggestions for more adequate dosing schedules based on patient status, can lead to the prevention of medication errors and an increase in treatment efficacy^20,23. One of the first studies that demonstrated the prevention of medication-related adverse events through a computerized alert program was developed by Evans et al.²⁴. This showed a significant reduction of adverse reactions, through the generation of alerts about allergies to drugs during their prescription. More recent studies describe improved systems, where a reduction in medication errors has been observed²⁵, as well as an improvement in the adequacy of prescription to the clinical status of patients²⁶, a reduction in drug dosing errors²⁷, and even a reduction in hospital stay²⁸. More specific studies have shown that the implementation of Clinical Decision Support Systems can prevent patients from receiving a drug dose contraindicated for their renal function²⁹; allow patients to receive the adequate treatment for venous thromboembolism prophylaxis with heparin³⁰; or help phisicians to select antibiotic regimen based on bacterial sensitivity and resistance³¹. These systems can also be designed with a focus on higher-risk populations, such as elderly patients³², paediatric patients³³, patients admitted to Intensive care units³⁴, or polymedicated patients with a higher likelihood of medication errors³⁵.

During recent years, pharmacogenetics has been incorporated to these systems, in order to increase safety in the prescription process for high-risk drugs³⁶.

 

CDSS design and implementation

These systems started being implemented over 20 years ago, and there has been a higher research in the United States³⁷. Even though their implementation was planned as a goal to be achieved rapidly, driven by the healthcare authorities in United States, their progress rate has been slow, partly due to the complexity of their development^16,38. Currently, initiatives are being promoted in many countries in order to address this challenge.

In Spain, there's been less experience published regarding the implementation of these systems than in other countries; that is why some work groups from the Spanish Society of Hospital Pharmacy (SEFH) are conducting efforts to increase their development^39,40.

In fact, one of the objectives of the strategic lines of the 2020 Group of the SEFH is that 80% of hospitals can have an Assisted Electronic Prescription System, connected and / or integrated into clinical records, which will include databases with medication information in order to make clinical decisions. According to the results of the initial survey conducted by this group in 2010, only 5.4% of the hospitals studied had partial access to this technology⁴¹.

During the past years, different support systems for prescription and pharmaceutical validation have been developed in hospitals in Spain, such as the "HIGEA" system, implemented in the Hospital Gregorio Marañón (Madrid, Spain)⁴², or the "CheckTheMeds"⁴³ or "Alto-Medicamentos"⁴⁴ applications, available in different Spanish hospitals, such as the Hospital Ramón y Cajal in Madrid or the Hospital Nacional de Parapléjicos, respectively.

 

Key points for CDSS design and implementation

Three stages must be considered for CDSS design and implementation, which are summarized below in figure 1:

1. To set up a multidisciplinary work team

The creation of a multidisciplinary work group formed by different professionals is essential for CDSS design, implementation, maintenance and monitoring. It is important that the IT professionals involved in the design / creation of these tools are part of this team, as well as their users (physicians, pharmacists, and any healthcare professionals involved), who can point out the requirements to be developed. There must be an on-going maintenance of the databases supporting the system, and the information and alerts in the system must be decided by consensus among all the members of this team^38,45.

The Pharmacist must be the leader of this work team. Their technical knowledge about information systems applied to the process of medication use, and their active participation with other healthcare and non-healthcare professionals in this setting, will be a key factor for the most adequate selection of technologies, assessing the value and contribution of each one for the improvement of the healthcare process. The Pharmacist holds an extraordinary position in order to address the selection and evaluation of those systems better suited for each situation, being a member of the design and implementation work teams, as well as the spokesperson among users, designers and programmers, and assessing the outcomes of implementation and the effect on patient care that these systems can offer⁹.

2. To define functional characteristics

• The following requirements are necessary in order to design and implement a CDSS:

• Assisted Electronic Prescription

• Computerized Clinical Record

• A computerized system able to identify / interpret the clinical data required to create decision criteria. Once these requirements are ensured, the design of

CDSS must consider 4 essential parameters: source data, alert "triggers" or parameters, interventions, and alternatives to be offered^46,47 (Figure 2).

For example, the elements required to implement a CDSS for drug dosing in renal impairment cases would be⁴⁸:

• Source data: Medications that need dose adjustment by renal function, cut-off level of creatinine clearance for drug dose adjustment, and recommended dose of the drug when there is renal function impairment.

• Alert trigger or parameter: Value of creatinine clearance available from the most recent lab test, related to the episode of patient admission / care.

• Intervention: A warning sign that the drug prescribed requires dose adjustment for renal impairment. The method and time of intervention will be variable and modifiable: it could be at the time of drug selection, at the time of dose prescription, or only as an informative message.

• Alternative to be offered: Information about the dose recommended based on the creatinine clearance of the patient.

To become established, the design and characteristics of these systems can adapt adequately to the prescription systems existing in the centre. The implementation of these computerized systems cannot be conceived as a static system, but as a complex and dynamic system that must be coordinated with the prescription process. The software must fit perfectly within the clinician work flow, it must be intuitive, and maximize time saving by its use, acting on real time.

It is essential to ensure that the "5 rights" or "5 truths" of CDSS are met⁴⁹:

• The right information,

• The right person / professional,

• The right intervention format,

• The right way of communication,

• The right time during the work flow

Special caution is required in order to avoid an excessive issue of alerts in irrelevant clinical situations ("alert fatigue"), because a continuous interruption of the work flow of the prescriber will lead to a higher overall omission of alerts, which could even include those which are considered more important^50,51.

3. Monitoring and follow-up of CDSS functioning

The main objective of CDSS monitoring and follow-up is to ensure the safety of these systems. But monitoring of all aspects associated with alert generation is also essential in order to assess their clinical impact and efficiency. Some of the parameters to be assessed are: the adequate functioning of the tool, its adequacy for the clinical setting of the patient, the acceptance of the alternatives suggested by the system or the time to response¹⁴.

Table 2 shows a series of variables that can be used as indicators of the adequate functioning of the tool, and that can help to measure the efficiency of these systems:

Difficulties in implementation

CDSS design and implementation has not evolved as expected, due to the existence of certain barriers or difficulties:

• Lack of interoperability and integration between systems, which interferes with the adequate collection and interpretation of the clinical data to be used. The integration of different systems means that these should work jointly by sharing data, while interoperability allows different systems to interact exchanging data from different sources. Thus, the optimal objective is integration between systems, which is the most difficult to achieve. In some cases, achieving system interoperability will be enough for implementing certain CDSS.

• Difficulty to manage clinical information originated in the different databases. This is mainly due to the lack of standardization of the terminology used, which will sometimes lead to obtaining data from free text or natural language, hindering the processing of patient information in order to alert adequately.

• Constant maintenance and update of information, with changes required according to the use and assessment of alerts.

• The high cost of implementation and maintenance.

• Insufficient evidence available. Most of the experiences published are based on evaluations of a single centre that has developed and implemented a CDSS internally. Added on to the heterogeneity of the studies in terms of methodology, variables studied, etc., this leads to a situation where the data available are not enough to ensure that the usefulness of these systems can be extrapolated to other centres⁵².

• Likelihood of causing negative consequences in patient safety. The evaluation of CDSS implementation has not always demonstrated an improvement in healthcare outcomes. Even though the system has been designed to avoid mistakes, once implemented there can be unexpected consequences during its use, and it could even have a direct effect on patient safety, generating new types of errors^53,54. A study conducted by Strom et al., where an alert system was implemented to reduce the concomitant administration of warfarin and cotrimoxazole in order to prevent interaction, had to be interrupted prematurely because this intervention led to a delay in the treatment of 4 patients which was considered unacceptable by the evaluating clinicians⁵⁵.

• One of the main causes for the non-desired effects of CDSS is alert omission. There are studies describing omissions in 49 to 96% of cases¹⁷. The main reasons for the low response by clinicians to automatic warnings is the great number of low-relevance alerts ("alert fatigue") and their poor design. Clinicians tend to dismiss alerts due to their lack of specificity, and because they think that the additional clinical knowledge of the situation within the patient setting is missing¹⁹.

Future development of CDSS

In the future, efforts must be directed towards looking for solutions to these barriers that hinder CDSS development.

In this sense, we have new technological advances in health systems, which can help to improve some of the difficulties found (Table 3).

1. Interoperability and integration

Interoperability and integration between IT systems has been an obstacle difficult to overcome so far. The exchange of clinical information is basic in order to obtain solid prescription support systems. For this reason, there are world organizations, such as HL7, focused on developing global standards in order to facilitate the electronic exchange of information. Over 50 countries participate in this organization; its objective is to provide standards for the exchange of clinical, healthcare, administrative and logistic information between the different health systems⁵⁶. HL7Spain, the organization which represents Spain in the international setting, regulates and adapts world standards to the national setting, and promotes the use of this standardization. Their "Guidelines for Implementation in the Pharmacy" stand out: a data model is described for information exchange between the different systems involved in drug prescription, dispensing and administration, as well as their involvement in a standardization document for the implementation of the electronic prescription⁵⁷.

"The Microsoft Health Common User Interface"(MSCUI)⁵⁸ is another recommendation guideline about the normalization of elements in the clinical information systems. These guidelines, developed by Microsoft with collaboration by the Joint Commission, are based on a series of safety principles and recommendations on normalization, so that healthcare professionals can exchange information between the different applications, therefore increasing the efficacy of clinical information and the improvement of patient safety.

Other strategies to solve the interoperability problem consists in the use of a common terminology in all systems. One example would be SNOMED CT (Systematized Nomenclature of Medicine - Clinical Terms), an international clinical terminology which contains over 300,000 concepts and over 700,000 descriptions organized in hierarchies and associated between them. These concepts feature a number code which allows computerized systems, even if not designed for this aim, to receive and integrate information coming from other sources in an understandable way, taking into account their original meaning, without requiring human intervention. SNOMED CT, developed by the IHTSDO (International Health Terminology Standards Development Organisation)⁵⁹, is being used in over 50 countries all over the world. The IHTSDO also works jointly with the WHO and HL7 in order to code their standards with this terminology⁶⁰. In Spain, SNOMED CT is already being used in many normalization projects for clinical information systems^61,62 and can be used to code the medication available, and thus have a linking code between the different medication databases⁶³.

Using these strategies, we can grant CDSS a more effective access to the clinical information with higher quality patient care, and contribute to reduce the costs associated with the implementation.

Smart phones and tablets are getting positioned as new hardware devices that can participate in the clinical care process. These devices provide information in a personalized and immediate manner, offering fast communication between users. During recent years, there has been an increase in the use of applications focused on providing help for clinical care: clinical calculators, guidelines for drug use / information, interactions, diagnosis, etc^64,65. The connectivity of these applications with clinical data could increase their cost-effectiveness, allowing automatic access to the necessary data and alert systems, and providing an extra bedside point of care. However, a comprehensive regulation and encryption process must be followed, in order to guarantee the security of the data entered, both personal and from patients, and ensuring a secure interface between the different operating systems⁶⁶.

2. Smart systems for clinical information management

The complexity of clinical information within the healthcare system, its variability over time, and the high heterogeneity of the data contained, is difficult to formalize and represent in accurate computerized models. However, new smart tools have been recently appeared, which allow to improve the management of this type of data, and design dynamic and continuous systems with the ability to learn from themselves.

Ontologies appear as a way to solve this problem⁶⁷; these are structures of knowledge where a set of concepts and the semantic links between them are formally represented. Ontologies are explicit structures, and represent knowledge in a format that can be read by machines, and that humans are able to understand. The use of ontologies is growing rapidly in the setting of biomedicine, allowing the creation of databases with knowledge and links between biomedical terms that can help to optimize CDSSS. There are different examples in clinical practice, such as the support system for antibiotic therapy prescription by Bright et al., which uses an ontology where patient data, sensitivities and resistances are entered, in order to provide the most adequate antibiotic therapy in each case³¹. These are also being used in order to improve the alert systems for pharmacological interactions in polymedicated patients ⁶⁸, to create adverse effect databases, or even to create tools for medication conciliation^69,70. These tools provide an improvement in the use of computer systems and databaeses, in order to have more robust and better linked information, and provide more reliable data in the patient care process.

Another tool which can be useful for obtaining the clinical information necessary for the use of CDSS is the Natural Language Processing (NLP). Many of the data included in clinical records are expressed through text editors and not coded; therefore, it is difficult for our currently available CDSS to incorporate the information included. NLP tools allow to extract automatically the information elements from sources that are not designed for computer extraction, structuring them in a codified manner, and understandable for computer systems. With promising results, these tools have started to be used to prepare databases of medications linked with their indications, based on different information sources^71,72. The association between patient health problems and the drugs in the electronic clinical record can be used to improve the quality of information and reduce the medication errors associated with the indication of these drugs. These tools could be used to make CDSS more specific at the time of alert, because they would be able to obtain the clinical information required to ensure the adequate use of a drug.

The incorporation of these new computer applications can allow us to create more robust systems that will help us to monitor patient clinical parameters, with the necessary information, alerting only in those cases in which patient safety can be at risk, providing treatment as individualized as possible, and avoiding alert fatigue. Besides, those smart systems that incorporate information automatically will allow a more efficient maintenance, with lower need for updates, and a reduction in information errors. Figure 3 shows an example of the interrelation between the different prescription support tools available, and the clinical workstation.

3. Creating awareness about CDSS

The publication of the follow-up and evaluation outcomes of the functioning of these tools can help to optimize their implementation. Making these outcomes public, both among the users of the tool as well as for general scientific knowledge, is essential in order to promote and improve the development of these systems. Scientific societies and work groups specialized in new technologies will have a critical role in this transmission of knowledge.

 

Conclusions

The experience published so far supports the use of CDSS as competent tools in the setting of patient safety. However, in order to obtain an efficient implementation of these systems, it will be essential to focus all efforts in overcoming the barriers that hinder their widespread use, to share the knowledge and experiences conduced in different centres, and to conduct studies that evaluate the effect of CDSS on clinical outcomes, workload and process efficiency, satisfaction of patient and users.

The design of CDSS must be adapted to the context and setting where they will be used. It is essential to take into account the workflow of professionals, and avoid alert fatigue. The adequate monitoring of the entire process, evaluating variables of use and response can help to enhance the implementation, with a high level of adherence to recommendations.

The incorporation of new technologies and smart systems will provide more reliable and concise information, which can increase the efficacy of these tools. Different CDSS have been currently developed, but there is a great heterogeneity among them. Once the problem of interoperability between systems has been solved, and making the most of the common exportability patterns, we must be able to use and evaluate CDSS at a multicentre level, thus making these evaluation studies more robust, and with the potential to be extrapolated to other centres.

The creation of a multidisciplinary group within the healthcare institutions and centres is an essential element in order to address the implementation of these new technologies. The pharmacist must play a leading outstanding role in the process of CDSS development and implementation. Their knowledge about drugs and their use, and the fact that the pharmacist leads the safety programs for medication use and, on many occasions, the management of the quality systems of the hospital, provide them with the knowledge required to be the leader in this new line of work.

 

Funding

Article without funding.

 

Conflict of interests

The authors declare not having any conflict of interests in terms of writing this article.

 

Acknowledgements

We are sincerely grateful for the collaboration by the TECNO Work Group in the preparation of this article, for all their comments and contributions.

 

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Correspondence:
Correo electrónico: solanuria13@gmail.com
(Núria Solà Bonada).

Recibido el 4 de enero de 2015;
aceptado el 16 de septiembre de 2016.