Health information programs are essential in ensuring efficiency at all levels. The ability to implement better EHR systems is dependent on the ability to accurately measure the productivity and costs of the system while guaranteeing various issues are such as patients information are confidential. This paper proposes a change in EHRs to improve the all these services and improve efficiency and effectiveness of data coding, choice of systems, data registry, payments and any other issues at the cheapest costs. The accomplishment of such objectives is dependent on the analysis of the system, accuracy, information operability, data storage and health information systems, clinical indices, and finally the data evaluation from diverse sources to create meaningful presentations. The six categories will enable understanding health information management and the best implementation of EHRs systems and coding.
Que. 1: Evaluate, implement and manage electronic applications/systems for clinical classification and coding
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Clinical classification and coding are essential for the transfer and handling of patient’s data, payments, stakeholders, and limitation of errors which can result in erroneous prescriptions. According to AHIMA the size, the number of patients, and IT is crucial when determining the most suitable clinical classification and coding application to employ. Different information systems, for instance, computer-based and coders are mostly used in recording and data storage. Computer-based information systems use computer systems known as decision support systems (DDSs) when making important decisions. These systems use management information systems (MISs) to manage budgets, perform employee evaluations, and create work schedules among other tasks. Encoders use transaction systems whereby recording is in the form of codes thus helping other tasks while using the codes to either submit bills to third parties among different stakeholders. Computer-assisted coding (CAC) is becoming one of the most popular applications in clinical coding as it helps improve the productivity of impending transitions from ICD-9-CM to ICD-10-CM/PCS (AHIMA Group. 2013). CAC technology is automated and does not result in employees losing their jobs even if most of the system is automated. Unlike other systems that use natural language processing (NLP) for validation by coding experts, CAC is structured to integrate the NLP coding into clinical documentation process thus ensuring clinical documents with embedded codes. It is, therefore, simpler compared to the sophisticated algorithms in NLP which read documents and generate codes.
CAC creates a visual representation of the current workflow and helps in defining the future state by presenting where the highest efficiencies could be achieved. It requires a valid and known vision with a particular team that includes every employee and external influencers such as vendors. NLP complex algorithms make it difficult for most of the employees making its efficiency to only depend on IT experts thus impossible to predict the future and ensure inclusivity in improving the system as it eliminates most of the working team. The success of CAC in improving ICD-9-CM or ICD-10-CM/PCS is dependent on its measurability and auditing (AHIMA Group. 2013). The implemented CAC must be able to depict the changes in cost-benefit scenarios, the changes in the productivity to determine whether the system is an improvement or detriment to the system. The vendors are part of the implementation of their costs and services are vital in successful implementation. CureMD and Athenahealth are some of the best vendors. CureMD uses a consumer-based strategy to ensure it supports the purchasers and it bundled suite along the billing service that is own in-house clearinghouse makes it attractive for all types of hospitals or users. Its 10G makes its interface to have high usability. Athenahealth on the other hand is a web-based and ranked among the top ten of the best vendors however its pricing and poor sales teams can be indifference which makes it undependable. It can only be used for both EHRs and PM services hence if a person needs a standalone EHRs or PM it is inappropriate. The two vendors are most suited for the tasks, but Athenahealth pricing policy is detrimental to small and medium-sized practices thus making CureMD the most adapted to implement the CAC.
Que. 2: Evaluate the accuracy of diagnostic and procedural coding
ICD-10 is an improvement of the ICD-9 and is based on either ICD-10-CM or ICD-10-PCS which represent the diagnosis and procedures respectively. It replaced the ICD-9-CM and is the most used by U.S. healthcare. Health providers select codes on documentation from the patient’s medical records, and the codes are maintained by the CDC. The MAC is used to determine coverage and also procedure codes for discharge to the MS-DRG ("ICD 10 vs CPT vs HCPCS Code Sets", 2018). The ICD-10-PCS is used for U.S. inpatients whereby the physicians and service providers do not use code-set reports for in ambulatory services, but they can select codes based on patient’s medical records.
HCPS is divided into Level I and Level II CPT Codes and modifiers that do not include products, services and supplies. The AMA developed and maintains the code set, but in Level II HCPCS CMS maintains the code set for most services with the exclusion of dental services ("ICD 10 vs CPT vs HCPCS Code Sets", 2018).
The two systems are incomplete in the coding and recording of patient’s medication reports thus providing the loopholes that increase errors in auditing, accurate diagnostic and coding. In the above description, it is evident that under these CDI programs, there are services, products and supplies that are not recorded for instance the lack of ambulatory services in the ICD-10 which can implicate the medical records of the patients whereas the HCPCS does not record supplies of essential products such as medicine. HCPCS is also dependent on ICD-10 for most of the procedures thus making it difficult to be used separately and may lead to data duplicate or over recording (Sayles, n.d.). The different coding makes it challenging and hectic to determine an era when it occurs in either of the systems. ICD-10-CM/PCS can be improved through the implementation of CAC to automatize most of the coding, however, the changing of codes can result in err if many changes in code entry are entered thus leading to incorrect and misleading codes.
Que. 3: Advocate information operability and information exchange
Information exchange is essential in increasing the diagnosis that can establish a new epidemic or improve the CDC records of the illnesses and the best medications. The PDMP reports into the health information technologies, for example, HIE, PDS or EHRs provide the providers with streaming access and improves the comprehensiveness of health provisions and interstate medical records sharing. Although the information sharing is beneficial, it poses risks such as patient’s privacy and confidentiality. The HIE interoperability is dependent on incorporated MPI to manage and cross-reference identities of patients.
Some of the critical components are document repository, registry, cross-community gateway and the patient’s identity. These components are the causes of confidentiality problems (Farzandipour, & Sheikhtaheri, 2009). The use of encryptions and pseudonymization provide security to personal data and medical records. Interoperability can be solved by separating patient’s registry that uses the same PIM infrastructure by allowing the identifiers to be linked back to the relevant systems.
Que. 4: Evaluate health information systems and data storage design
HIS are in the gamut from patient-specific clinical ISs or financial systems to fully integrated systems. The systems are categorized into clinical information, administrative information, management support, and research and data analytics. Clinical information ranges from EHRs, patient care management, computerized provider order entry (CPOE) to other services that are patient-oriented services. Management support systems are based on patient registration and financing modelling whereas research and data analytics systems include study diseases.
Data warehouse contains historical data that are derived from transaction systems and is optimized for fast retrieval of data. It essential that information is stored and retrievable, when needed without the threats of data, lose to ensure simplified reporting, complex analysis and multidimensional analysis. Onsite, cloud and hard drive storage are the most common types of data warehouses as they store data and can be retrieved through the internet or intranet data mining techniques (Sayles, 2013). Onsite and cloud storage are quite similar although cloud storages store large data. The risks include hacking of websites thus can result in privacy issues and the essence it is difficult to delete medical reports from the cloud. Hard drive and CD images can also store data but the data is only retrievable through physical location thus limited by geography and storage life is even shorter compared to web-based storage platforms.
Que. 5: Manage clinical indices/databases/registries
Clinical indices and registries are used to ensure reduction of managerial challenges such as leadership, confidentiality, and technical issues. These issues are solved through biometrics, master patient index, and matching algorithms. Biometrics are emerging techniques that use automated individual based such as fingerprints, hand geometry, and vascular pattern. It is the most secure way of enhancing privacy and security. Master patient index (MPI) links patient’s clinical information with a particular institution (Gliklich, Dreyer, & Leavy, 2014). The patients treated in an institution can be traced back to the institution’s database. Matching patient’s algorithms are essential in linking projects and facilitating technicality of population-based research.
Que. 6: Evaluate data from varying sources to create meaningful presentations
Data Mart, a subset of data warehouse, is designed for the single purpose or specialized use. It is cost effective due to its ability to narrow down data analysis as it targets a smaller target area. Online analytical processing (OLAP) allows the user to retrieve specific information from the extensive database thus transforming the data warehouse into a decision support tool and is used for operational data used in strategic decisions (Sayles, 2013). Lastly, data warehouses retrieve data from pharmaceutical manufacturers’ research and marketing; insurance companies use the data warehouse to form clinical repositories.
References
AHIMA Group. (2013). Automated Coding Workflow and CAC Practice Guidance (2013 update). Library.ahima.org . Retrieved February 15, 2018, from http://library.ahima.org/doc?oid=300265
Farzandipour, M., & Sheikhtaheri, A., (2009). Evaluation of Factors Influencing Accuracy of Principal Procedure Coding Based on ICD-9-CM: An Iranian Study. Perspectives in Health Information Management / AHIMA, American Health Information Management Association , 6 , 5.
Gliklich R. E., Dreyer N. A., & Leavy M. B., (2014). Registries for Evaluating Patient Outcomes: A User's Guide [Internet] . 3rd edition. Rockville (MD): Agency for Healthcare Research and Quality Managing Patient Identity Across Data Sources. Retrieved February 15, 2018 from: https://www.ncbi.nlm.nih.gov/books/NBK208618/
ICD 10 vs CPT vs HCPCS Code Sets . (2018). Icd10coded.com . Retrieved February 15, 2018, from https://icd10coded.com/code-set/
Sayles, N. B. (2013). Health information management technology: An applied approach . L. L. Gordon (Ed.). American Health Information Management Association.
