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CHAPTER ONE

INTRODUCTION

1.0 INTRODUCTION

Clinical decision support is any system designed to improve clinical decision making related to diagnostic or therapeutic processes of care. Clinical decision support systems are often computer-based, which allows the user to take advantage of the capacity of computer systems to process information from the patient record and to deliver appropriate recommendations to providers at the point of care (Pearson et al., 2009). Clinical decision support can support the delivery of high-quality healthcare by providing intelligently filtered, patient-specific knowledge at the point of care. Clinical decision support encompasses a variety of tools and interventions such as computerized alerts and reminders, clinical guidelines, order sets, patient data reports, and dashboards, documentation templates, diagnostic support, and clinical workflow tools (Garg et al., 2005).

According to Murphy (2014), clinical decision support is not simply an alert, notification, or explicit care suggestion. Clinical decision support encompasses a variety of tools including, but not limited to: computerized alerts and reminders for providers and patients; clinical guidelines; condition-specific order sets; focused patient data reports and summaries; documentation templates; diagnostic support; and contextually relevant reference information. These functionalities may be deployed on a variety of platforms (e.g. mobile, cloud-based, installed). Clinical decision support is not intended to replace clinician judgment, but rather is a tool to assist care team members in making timely, informed, higher quality decisions.

Holroyd et al. (2009) asserted that clinical decision support applications range from electronically available clinical data (e.g., information from a clinical laboratory system or information from a disease registry), electronic full-text journal and textbook access, evidence-based clinical guidelines, and systems that provide patient and situation-specific advice. Recognizing the potential for Clinical decision support to help ensure the safety and quality of health care, and the need to develop consensus about the use of Clinical decision support to promote safe and effective care in the society, there is need for its adoption.

Eberbardt, et al. (2012) opined that clinical decision support can be provided in various ways including, but not limited to, interruptive activities such as “pop-up” alerts, information displays or links (such as InfoButton), and targeted highlighting of relevant data. The key is that the information be presented when relevant, to those who can act on the information, and in a way that supports completion of the right action. While many providers may associate Clinical decision support with pop-up alerts, alerts are not the only or necessarily the best method of providing support. For example, a pop-up alert can only fire after an event has occurred (e.g., a provider has ordered a contraindicated medication). One proven example of Clinical decision support is for abnormal blood pressure readings to automatically appear in red text (as opposed to normal blood pressure readings that appear in black) on providers’ displays. This method supports clinical workflow, but does not interrupt the provider’s thought process or risk that an alert will be ignored due to ‘alert fatigue’ which has been identified as a key concern for implementers of CDS programs (Eberbardt, et al. 2012).

Kawamoto, et al. (2005) explained that a clinical decision support system is intended to improve healthcare delivery by enhancing medical decisions with targeted clinical knowledge, patient information, and other health information. A traditional clinical decision support system is comprised of software designed to be a direct aid to clinical-decision making, in which the characteristics of an individual patient are matched to a computerized clinical knowledge base and patient-specific assessments or recommendations are then presented to the clinician for a decision. Clinical decision support systems today are primarily used at the point-of-care, for the clinician to combine their knowledge with information or suggestions provided by the clinical decision support system.

Codish and Shiffman (2005) highlighted that clinical decision support systems have been endorsed by the US Government’s Health and Medicare acts, financially incentivizing Clinical decision support implementation into Electronic health records. In 2013, an estimated 41% of U.S. hospitals with an Electronic health records, also had a Clinical decision support systems, and in 2017, 40.2% of US hospitals had advanced Clinical decision support capability. Elsewhere, adoption rates of Electronic health records have been promising, with approximately 62% of practitioners in Canada in 2013. Canada has had significant endorsement from the government level, as well as Infoway, a not-for-profit corporation. England has also been a world leader in healthcare IT investment, with up to 20 billion euros invested back in 2010. Several countries have also managed to implement national health records, at least for patient-facing data, including Denmark, Estonia, Australia, and others.

According to Lee, et al. (2015), clinical decision support systems encode clinical knowledge into computerized algorithms and combine them with patient-specific data to provide clinicians with information and decision guidance. When successfully implemented, the ability of a Clinical Decision Support system to provide patient-specific decision support empowers health professionals to make timely decisions at the point of care while reducing medical errors. Another benefit of this technology is that the transformation of clinical knowledge into algorithms allows for the correction of areas where documents (eg, clinical practice guidelines) are ambiguous or unclear. Equipping emergency providers with basic skills and competencies in palliative care, commonly termed primary palliative care, affords an opportunity to align care trajectory with patient goals. Clinical Decision Support systems have been implemented to support care in several specialties, both in developed and developing countries.

1.1 BACKGROUND OF THE STUDY

Sola et al. (2014) noted that there have been multiple attempts through history to construct a computer or program, which would assist clinicians with their decisions concerning diagnosis and therapy. Ledley and Lusted published the first article evolving around this idea in 1959.

Furthermore, Sola et al. (2014) detailed that F. T. de Dombal and his co-workers at University of Leeds developed Leeds abdominal pain. It used Bayesian reasoning on basis of surgical and pathological diagnoses. These pieces of information were gathered from thousands of patients and put into systems’ database. The Leeds abdominal pain system used sensitivity, specificity and disease prevalence data for various signs, symptoms and test results. With help of Bayes’ theorem it calculated the probability of seven possible diagnoses resulting in acute abdominal pain: appendicitis, diverticulitis, perforated ulcer, cholecystitis, small-bowel obstruction, pancreatitis, and nonspecific abdominal pain.

In addition, Dehghani et al. (2018) explained that the system developed by F. T. de Dombal and his co-workers assumed that each patient with abdominal pain had one of these seven conditions, thus selected the most likely diagnose on the basis of recorded observations. Evaluation of the system was done by F. T. de Dombal and his co-workers in 1972. It showed that the clinicians’ diagnoses were correct in only 65 to 80 percent of the 304 cases, whereas the program’s diagnoses were correct in 91.8 percent of cases. Surprisingly, the system has never achieved similar results of diagnostic accuracy in practice outside the Leeds University. The most likely reason for that is the variation of data that clinicians entered into the system for acquiring correct diagnoses (Sola et al., 2014).

Moreso, Moja et al. (2014) added that Internist-I was an experimental CDSS designed by Pople and Myers at the University of Pittsburg in 1974. It was a rule-based expert system capable of making multiple, complex diagnoses in internal medicine based on patient observations. The Internist-I was using a tree-structured database that linked symptoms with diseases. The evaluation of the system revealed that it was not sufficiently reliable for clinical application. Nevertheless, the most valuable product of the system was its medical knowledge base. This was used as a basis for successor systems including CADUCEUS and Quick Medical Reference, a commercialized diagnostic CDSS for internists.

Cox et al. (2018) highlighted that clinical decision support systems have been endorsed by the US Government’s Health and Medicare acts, financially incentivizing Clinical decision support implementation into Electronic health records. In 2013, an estimated 41% of U.S. hospitals with an Electronic health records, also had a Clinical decision support systems, and in 2017, 40.2% of US hospitals had advanced Clinical decision support capability. While Vecchio et al. (2018) added that the adoption rates of Electronic health records have been promising, with approximately 62% of practitioners in Canada in 2013. Canada has had significant endorsement from the government level, as well as Infoway, a not-for-profit corporation. England has also been a world leader in healthcare IT investment, with up to 20 billion euros invested back in 2010. Shoa et al. (2015) stated that several countries have also managed to implement national health records, at least for patient-facing data, including Denmark, Estonia, Australia, and others.

1.2 STATEMENT OF PROBLEM

Over the past decades, numerous studies have shown that the quality of health care is inadequate in developing nations like Nigeria, and healthcare organizations are increasingly turning to clinical decision support systems to address this problem. To address these deficiencies in health care, healthcare organizations are increasingly turning to clinical decision support systems that provide clinicians with patient-specific assessments for recommendations to aid clinical decision making. Thus, this study seeks to provide an architectural system design that when implemented will aid our local health care providers in optimally serving patients at speed.

1.3 AIM AND OBJECTIVES

The study aims to optimize health care delivery by creating a clinical decision support system tool to aid health care clinical recommendations. The objectives of the study are stated as follows:

To design a clinical decision support system framework that will assist in health care given a patient health record
To implement a clinical decision support system in a local health facility where patients’ health history/records are readily available.
To provide a platform for patients to monitor their body mass index regularly

1.4 SCOPE OF THE STUDY

The clinical decision support system that will assess patients’ body mass index was designed using the Delta State Polytechnic, Otefe-Oghara Health Centre facility as use case.

1.5 SIGNIFICANCE OF THE STUDY

Recognizing this potential to improve health, this study will provide immerse benefits in assisting the patient care among students, health workers and the general public and thus it is important to individual patients, health practitioners, the government and even the health monitoring authorities.

1.6 LIMITATION OF THE STUDY

This study would have reached broader scope but due to financial, human resources and time constraints, it was limited to using the polytechnic health facility and serving as a baseline for future research or implementation guide to other studies of the same subject matter.

1.7 DEFINITION OF TERMS

Care Advice: Advice provided to the patients for management of symptoms

Clinical Commissioning Group: Clinical Commissioning Groups commission most of the hospital and community services in the local areas for which they are responsible. Commissioning involves deciding what services are needed for diverse local populations, and ensuring that they are provided. At its simplest, commissioning is the process of planning, agreeing and monitoring services.

Clinical Document Architecture: CDA is an information and messaging standard

Clinical Decision Support System: A clinical decision support system (CDSS) is a health information technology system that is designed to provide physicians and other health professionals with clinical decision support (CDS) with clinical decision-making tasks.

Clinician: Medically qualified system users who triage patients through the CDSS

Health Information: General information provided about health and care with the absence of triage or symptoms

Clinical outcome: The result of receiving medical advice or treatment, and can be measured through subsequent patient activity data such as hospital admission or re-admission.

Electronic health record: An electronic health record is the systematized collection of patient and population electronically stored health information in a digital format.

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Full Project – Design and implementation of clinical decision support system

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Full Project – Design and implementation of clinical decision support system

Full Project – Design and implementation of clinical decision support system

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