CHAPTER ONE

INTRODUCTION

1.1 Background to the Study

Health Information Systems(HIS) provide the bedrock for decision-making and has four key

functions: data generation, compilation, analysis and synthesis, and communication and use. The HIS gathers data from the health sector and other relevant sectors, analyzes the data and ensures their overall relevance, quality, and timeliness, and converts data into information for health-related decision-making.In addition to being essential for monitoring and evaluation, the information system also providesearly warning capability,supports patient and health facility management, facilitate planning, supports and stimulatesresearch, permits health situation and trends analysis, supports global reporting, andunderpins communication of health challenges to diverse users (WHO, 2009).

To improve the quality of medical care around the globe,efforts are being made to increase the practice of evidence-based medicinethrough the use of an HIS called Clinical Decision Support Systems (CDSS). Clinical Decision Support provides clinicians, patients, or caregivers with clinical knowledge and patient-specific information to help them reach decisions that enhance patient care (Osheroff, Teich & Middleton, 2011). The patient’s information is matched to a clinical knowledge base, and patient-specific appraisals are then communicated effectively at appropriate times during patient care. Some CDSS include forms and templates for entering and documenting patient information, and alerts, reminders, and order sets for providing suggestions and other support. The use of CDSS comes with many potential benefits. Importantly, CDSS can increase adherence to evidence-based medical knowledge and can reduce unnecessary variation in clinical practice. CDSS can also assist with information management to support the physicians’ decision making abilities, reduce their mental workload, and improve clinical workflows (Karsh et al., 2010). When well designed and implemented, CDSS have prospects that can improve health care quality, and also to increase efficiency and reduce health care costs (Berner, 2010).

Despite the promise of CDSS, there are several barriers that can hinder their development and implementation. Till date, Medical knowledge base is incomplete in part because of insufficient clinical evidence (Englander & Carraccio, 2014). Moreover, methodologies are still being designed to convert the knowledge base into computable code, and interventions for conveying the knowledge to clinicians in a way they can easily usein practice are in the nascent stages of development. Low clinician demand for Clinical Decision Support is another encumbrance to broader CDSS adoption. Clinicians’ lack of motivation to use CDSS appears to be related to usability issues with the Clinical Decision Support intervention, its lack of integration into the clinical workflow, concerns about autonomy, and the legal and ethical implications of adhering to or overriding recommendations made by the CDSS (Berner, 2010). In addition, in many cases, acceptance and use of CDSS are hinged uponthe adoption of electronic medical records (EMRs), because EMRs can include Clinical Decision Support applications as part of Computerized Provider Order Entry (CPOE) and electronic prescribing systems.

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