Diagnotes Platform: A Case Study

Quality physician documentation is not only essential to providing superior clinical communication, but also allows for the delivery of useful data that “supports quality metrics, acuity of care, billing, and accurate representation of medical conditions” (Rosenbaum et al., 2014). The Centers for Medicare and Medicaid Services (CMS) uses a system to classify Medicare patient’s hospital stays into various groups in order to facilitate payment of services called Medicare Severity-Diagnosis Related Group (MS-DRG). Some payers also use all patient refined (APR)-DRG reimbursement systems. MS-DRG groups are outlined by a specific collection of patient characteristics which include areas specific to the “principle diagnosis, specific secondary diagnoses,

Data collected provides the health care organization, providers, administrators and the patients with valuable information. Tools assist the organization by measuring the performance data that provide the information to improve the patient experience and improve their care. These tools engage the organization in self-evaluation on an ongoing basis. These tools also provide and effective method of containing costs and provides the means to meet the regulatory requirements to improve quality care. Tools allow organizations to provide a

Electronic Health Record (EHR) is an electronic version of a patients medical history, that is maintained by the provider over time, and may include all of the key administrative clinical data relevant to that persons care under a particular provider, including demographics, progress notes, problems, medications, vital signs, past medical history, immunizations, laboratory data and radiology reports (Ehlke & Morone, 2013). The incentives from both of this articles will result in the delivery of quality care to many individuals in

An interview with an Assistant Professor at Duke University Health System in the Department of Medicine, Maestro Care Provider Champion and Clinical Content Architect. This physician works to incorporate clinical decision support tools into the electronic health record at Duke Health System. He manages the best practice advisory committee that may provide a way to deploy alerts to clinicians at the point of care. Alerts with order sets and recommended actions are created and updated to notify providers of current patient care guidelines or patient safety concerns.

Although HIT can provide tools to help with decision making in regards to diagnosis, management of disease, treatment, and prevention, the current EHR’s do not have a link to support systems to help manage chronic care. Primary care practices must now shift their focus on healthy patients, as well as acute and chronically ill patients. With HIT a provider can effectively report the quality measures, however the current EHR’s cannot identify which patients may need particular services (3).

To improve healthcare in America, the Obama administration passed a law under the Health Information Technology for Economic and Clinical Health Act, to encourage the widespread use of Electronic Health Records (EHR). Under this act, Clinicians and hospital would receive incentives and reimbursement for the effective used of EHR in their practices. Electronic Health Records is an electronic version of a patient’s medical history that is maintained by the health provider over time. It includes relevant clinical data that is pertinent in improving quality of care, reducing medical errors and potential health care cost. For the purpose of this paper, two functions of EHR will be discussed: Computerized Physician Order Entry (CPOE) and Clinical Decision Support (CDS) tools. These two support tools have the potential to greatly improve quality care and to reduce health care cost.

To assess the quality of health care it is providing Quality healthcare depends on the availability of condition data. Poor documentation, imprecise statistics, and insufficient communication can result in errors and adverse incidents. Inaccurate data intimidate patient well-being and can lead to expand costs, inefficiencies, and poor presentation. Further, mistaken or incomplete data also discourage health information exchange and obstruct clinical research, production development, and quality initiatives. The impact of poor data on care is only increased by the implementation. A consequential electronic health record ameliorates the capability for healthcare providers to enact evidence-based comprehension management and decision making for

The IPPS program got its start from a Yale University study that was completed in the early 1970’s and implemented in 1983. “The initial charge for the Diagnosis Related Group (DRG) developers was to create a classification system that would monitor quality of care and use of services in a hospital setting.” (Casto 126)

Diagnostic coding is the translation of written descriptions of diseases, illnesses and injuries into codes from a particular classification. For diagnostic coding in medical practices, defined as the main reason for the patient’s encounter with the provider. Diagnosis codes are generally used as a representation of admitted episodes in health care settings. A number of diagnostic coding systems are currently implemented across the world to code the stay of patients within a typical health setting such as a hospital. Under the hospital inpatient rules, the principal diagnosis is listed first. This principal

The process of improvements in healthcare for the United States has been a continuous battle with many obstacles and challenges. With that being said, our health care system is now relying on the technology resources to develop and maintain an efficient electronic health record. Therefore, one of the major benefits of having an electronic health records would be the advance interoperability to communicate with other systems (Pamela K. Oachs, 2016). Although, one of the challenges with effective data exchange is due to the many different ways that terminology is used in the healthcare systems (Pamela K. Oachs, 2016). Under those circumstances, I am going to describe three different clinical terminology systems such as, Diagnostic and Statistical

Medical billing is the health care area selected for the discussion. Medical billing is the process of submitting and tracking claims with health insurance companies in order to receive payments for the services rendered by healthcare providers to ensure business finances success. (https://en.wikipedia.org/wiki/Medical_billing). Usually, the process is performed by electronic means and each claim must be a mirror image of the patient encounter. Although electronic billing carries sensitive information most practice management billing software relies on the clearinghouse which converts the data into HIPAA format. Thus, information is encrypted to minimize any unauthorized access. The actual diagnosis code system will be replaced by ICD-10 on October 1, 2015. Hence, the need for greater coding accuracy and specificity has heightened considerably since the implementation of ICD-9-CM (Bowman S., 2008).

Clinical decision-support systems (CDSS) apply best-known medical knowledge to patient data for the purpose of generating case-specific decision-support advice. CDSS forms the cornerstone of health informatics research and practice. It is an embedded concept in almost all major clinical information systems and plays an instrumental role in helping health care achieve its ultimate goal: providing high quality patient care while, at the same time, assuring patient safety and reducing costs. This computer based systems designed to impact clinician decision making about individual patients at the point in time that these decisions are made. If used properly, CDSS have the potential to change the way medicine has been taught and

Those working in the field of healthcare delivery have long recognized the importance of obtaining and recording accurate data during their course of their interaction with a patient, from the physical examination recorded by nursing staff to the administration of precise dosages by primary care physicians. For centuries, doctors the world over all shared a relatively reliable, yet admittedly simplistic method of storing and accessing this vital medical information: a paper chart, pencil, and clipboard. While scrawling a diagnostic conjecture or the result of blood test on a patient's "official" medical record sufficed until the dawn of the computing age, with the advent of mainframe databases in the early 1960s, digital data storage in the late 1990s, and cloud computing today, the concept of healthcare informatics has emerged to enable nurses, physicians, pharmacists, and other healthcare providers to efficiently gather crucial data and make accurate interpretations to effect positive patient outcomes (Hebda & Czar, 2009). Today, a growing consensus has developed among providers and patients alike which holds that "health information management (HIM) professionals are facing an unprecedented opportunity to help shape the future, not only of health information management, but also of healthcare delivery" (Abdelhak, Grostick & Hanken, 2012). Before one can take advantage of the enormous benefits afforded by a health informatics system,

Having a single view of the patient and their treatment and recovery plan is invaluable in ascertaining which are the most and least effective tactics in treatment. The 360-degree view of the patient and the many processes supporting them is crucial for increasing the accuracy, effectiveness and performance of treatment programs over time (Blakeman, 1985). Computerized management systems are critical for organizing, analyzing and translating the massive amount of data captured on patients, treatment and recovery processes, and the use of supporting IT systems to optimize patient health and organizational provider performance (Peshek, Cubera, Gleespen, 2010). The ability to aggregate and intelligently use all available data, information, patient-based and process-generated data to deliver higher levels of quality care is possible when computerized management systems are used throughout healthcare organizations.

A considerable amount of effort and time is required to maintaining workable databases for operational studies. The disciplines’ when applying any methods of analytical decision-support for understanding operational research, allows healthcare organization’s to manage better, because the limitations in resources involved with (HIS) health administration systems (Chow, Huang, & Puterman, 2009). In regards, to the lack of integration across various departments pertaining to (HIS) health information systems are challenges that healthcare facilities endure, that includes consistency and poor accuracy of data- system information. These examples of applications to include: clinical decision-making, reduction of emergency room congestion, and applications suitable for a diverse health environment are current challenges that the healthcare industry faces (Chow,