To: Dr. Phil

From:

Date:

Subject: Karen proposal

Information System Proposal

Introduction

Clinicians face a rising amount of clinical data and rising volumes of research on medical work as they treat patients. While many physicians use electronic databases and health record systems to manage the exponentially increasing amount of information, clinical decision support systems come in handy to boost efficient decision-making and ensure patient safety. However, the absence of an efficient decision support system results in shadows in the IT projects. Karen Clinic Company’s current internal information system has failed; it is inadequate and outdated. Hence, it requires a changeover to a new system that will upgrade the quality of service, operation stability, and enforce IT compliance.

Proposed Information System

My proposal is a clinical decision support system (CDSS) that considers clinical decision-makers’ cognitive functions and data interactions. The problem in context is the need to fix a new system that reduces or eliminates inadequacy and inefficiency leading to Shadow It functionalities in the current information system in Karen Clinic. The demand for the system is high because it handles multi-disciplinary tasks that require integration of large volumes of information in the clinical domain (Yao & Kumar, 2013). Notably, CDSS intends to monitor projects that fly under the radar without passing through the right formal channels such as IT governance. Thereby enables the executives to restrict and stop attempts of employees to install tools that can bypass the internal controls.

Functions Important to Business

CDSS information reflects the process for making decisions intellectually and contextually to boost the effort by clinicians. It makes dynamic predictions that consider the longitudinal nature of the disease, allowing the clinicians to interact with the system effectively. The system makes clinical decisions, which include predicting the patient’s prognosis, diagnosis, and selecting optimal treatments. The decisions are interdependent but reflect on data flow patterns (Shah, .2014) CDSS relates ordered steps that lead to new data significant to make decisions. Consequently, it improves IT governance in the sense that executives can control the installation and configuration of new systems that can cause outages due to lack of documentation. It would reduce the risk of undercover infringement to the information security system of the Company.

The system allows input of patient history, physical examination details, and symptoms, which directs the clinicians on what diagnostic test to take. Besides, the system offers a positive feedback loop for the construction of the decision paradigm. Also, it combines lab tests and diagnosis data to allow for accurate predictions of prognosis to patients. Additionally, the system has a high-level algorithm that encapsulates the communication characteristics of the Karen clinic. In the context of shadow IT projects, it restricts the development of data repositories to manage patient’s data as it is prone to losses.

Data Management

Health care professionals use cognitive skills to assess healthcare data. Critical skillsets are used to analyze differential diagnosis alongside experiential skills which combine clinical information to make clinical decisions. According to Hersh, data sharing is seamlessly evident in the interoperable clinical environment. Both the system and health care professionals share data to create a medical logic, and hence inter-professional collaboration is efficient at this point of view. Besides, data management employs a longitudinal insight. Many clinical procedures incur repetition; hence temporal longitudinal data establishes a foundation for predictive decision support. The system has a document management system that identifies knowledge repositories folders hidden from the organization to counter knowledge waste and losses.

Data Types

In a clinical setting, the health professional primarily uses data like patient records. Regardless of the hospital characteristics, the system’s absolute data are diagnostic test results, patients’ detailed health history, progress, prognosis, and treatment procedures. All are synchronized towards decision making alongside the health professional cognitive skills and knowledge. The records include the use of descriptions and figures. Common data types are character, integer and floating-point often stored in bytes range 0 to 8.

Storage Methods

Scanned documents, diagnostic results, patient records, and images from physicians are stored in the CDSS database system, where only authorized clinicians can access and retrieve the information captured in an organized manner. The CDSS uses a file storage system; data is aligned in a hierarchical structure to allow for easy navigation while others are stored in block forms. Some are stored as objects to allow quick configuration and easy retrieval. Nevertheless, it utilizes unstructured manner storage of physician’s notes and general patient’s requests. However, the system employs cloud storage that enforces data encryption. It becomes undecipherable to unauthorized users.

Data Quality

A high-quality health service requires proper handling of clinical information. CDSS considers extensive delineation of data quality, which includes correct and complete data in the system. Unstructured data leads to increased chances of inaccuracy and challenges like less concordance (Weiskopf,.2013). Another property for the system allows for reasonable information breadth and density that enhances the strength of prognosis prediction. The system has robust firewall technology that protects the data itself. It counters the insiders who attempt to bypass interior cybersecurity.

The transition of System Functions

Previously, the clinic used an electronic health record automation system. It intended to streamline the clinician’s workflow by easing access to patient records and information. It had many access points of data that increases the chances to breach of security protocol. Often there is inflexibility to retrieve data and specific representations of information. Consequently, the aforementioned inherent need to replace it drives the company towards the use of CDSS. It has tools that clinicians can use to access reports and patients data easily. The cloud system has a limitation to a few access points; it segments data to only a few trusted users enabling improved data security and elimination of shadow projects.

Evidence of feasibility

At the University of Utah, a study shows the use of a CDSS system is effective in data extraction; it is an easing tool for the decision making process. Data extraction improves the sensitivity of algorithms, enhancing prediction capabilities. However, it is more expensive than an electronic health record automation system because more money is spent on integrating cybersecurity. All Scripts is a similar CDSS system that is successfully proven efficient in over 800 clinical guides. The CDSS is based on decision-making processes compared to other information systems such as management information systems that collect and process information from other sources of the institution.

References

Hersh W. Secondary Use of Clinical Data from Electronic Health Records [Online]. Available from: https://dmice.ohsu.edu/hersh/secondary-use-trec.pdf

Yao W, Kumar A. CONFlexFlow: integrating flexible clinical pathways into clinical decision support systems using context and rules. Decis Support Syst. 2013;55(2):499–515.

Shah K. Case-study-an answer to analytical, clinical decision making. J Orthop Case Rep. 2014;4(2):3–4.

Weiskopf NG, Hripcsak G, Swaminathan S, Weng C. Defining and measuring electronic health records completeness. J Biomed Inform. 2013;46(5):830–6.