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Applying Science to Medicine

Applying Science to Medicine Only 55% of patients receive recommended care, with little difference found between care recommended for prevention, to address acute episodes, or to treat chronic conditions (McGlynn et al, 2003). The lag time between the discovery of more effective forms of medical treatment and their incorporation into routine patient care averages seventeen (17) years (IOM). Computerized provider order entry (CPOE) has been widely documented as a necessary tool to reduce preventable medication and other related errors but only 7.4% of acute care hospitals in the United States utilize CPOE with appropriate rules and evidence (HIMSS Analytics). The most fundamental building block for CPOE is the evidence based order set, but complexities associated with creating, managing and updating order sets have introduced major obstacles to CPOE implementation efforts. Chronic conditions such as heart disease, diabetes or arthritis affect more than 130 million Americans directly, and account for 7 in 10 deaths. Further, these chronic conditions consume 75% of all healthcare spending, and account for nearly two-thirds of the growth in health spending over the past 20 years -costing the U. S. economy $1 trillion annually (Almanac of Chronic Conditions, 2008 Edition). Estimates suggest the average patient upon hospitalization has 2.75 diagnoses - meaning "appropriate care" must span and synthesize multiple morbidity-specific best practices to effectively administer care to that "average" patient. The traditional approach to treating patients with evidence based protocols requires a physician to perform an ad hoc exercise of "mental merging" - reconciling duplicate candidate orders across multiple order sets to treat a patient with co-morbidities (today's norm). A more clinically effective, productive, and patient safety-centric alternative is to employ a proprietary software merging algorithm. These advanced algorithms remove duplicate orders, resolve conflicts, completes validation of the appropriate medical evidence and organizes the resulting merged order set so the physician can succinctly address the patients' often complicated treatment by focusing on the unique combination of labs, medications, etc. appropriate for the specific presenting conditions. This article describes a patent-pending propriety method of algorithmically merging multiple independent order sets for patients with co-morbid and chronic conditions into a single, maintenance free and evidence-based order set that can be immediately implemented into physician workflow to satisfy "Meaningful Use" guidelines for incremental provider reimbursement based on the American Recovery and Reinvestment Act (ARRA) legislation. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Innovation Science Emerald Publishing

Applying Science to Medicine

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Publisher
Emerald Publishing
Copyright
Copyright © Emerald Group Publishing Limited
ISSN
1757-2223
DOI
10.1260/1757-2223.2.1.1
Publisher site
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Abstract

Only 55% of patients receive recommended care, with little difference found between care recommended for prevention, to address acute episodes, or to treat chronic conditions (McGlynn et al, 2003). The lag time between the discovery of more effective forms of medical treatment and their incorporation into routine patient care averages seventeen (17) years (IOM). Computerized provider order entry (CPOE) has been widely documented as a necessary tool to reduce preventable medication and other related errors but only 7.4% of acute care hospitals in the United States utilize CPOE with appropriate rules and evidence (HIMSS Analytics). The most fundamental building block for CPOE is the evidence based order set, but complexities associated with creating, managing and updating order sets have introduced major obstacles to CPOE implementation efforts. Chronic conditions such as heart disease, diabetes or arthritis affect more than 130 million Americans directly, and account for 7 in 10 deaths. Further, these chronic conditions consume 75% of all healthcare spending, and account for nearly two-thirds of the growth in health spending over the past 20 years -costing the U. S. economy $1 trillion annually (Almanac of Chronic Conditions, 2008 Edition). Estimates suggest the average patient upon hospitalization has 2.75 diagnoses - meaning "appropriate care" must span and synthesize multiple morbidity-specific best practices to effectively administer care to that "average" patient. The traditional approach to treating patients with evidence based protocols requires a physician to perform an ad hoc exercise of "mental merging" - reconciling duplicate candidate orders across multiple order sets to treat a patient with co-morbidities (today's norm). A more clinically effective, productive, and patient safety-centric alternative is to employ a proprietary software merging algorithm. These advanced algorithms remove duplicate orders, resolve conflicts, completes validation of the appropriate medical evidence and organizes the resulting merged order set so the physician can succinctly address the patients' often complicated treatment by focusing on the unique combination of labs, medications, etc. appropriate for the specific presenting conditions. This article describes a patent-pending propriety method of algorithmically merging multiple independent order sets for patients with co-morbid and chronic conditions into a single, maintenance free and evidence-based order set that can be immediately implemented into physician workflow to satisfy "Meaningful Use" guidelines for incremental provider reimbursement based on the American Recovery and Reinvestment Act (ARRA) legislation.

Journal

International Journal of Innovation Science – Emerald Publishing

Published: Mar 1, 2010

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