The healthcare system will ensure the standardization of enterprise needs by integrating data dictionaries into the current electronic health record (Giannangelo, 2019). The data dictionaries will handle vocabularies, terminologies, and classifications. Data dictionaries collect descriptive names, meanings, and attributes of data elements in an information system or database. Standardized definitions enhance the business data’s authenticity by reducing duplication of patient information and data in the design and simplifying the data review method. The creation of data sets aids the compilation of information components into a consistent process. Data such as clinical/medical documentation, economical, demographic, qualitative, and insured/uninsured would be available to the hospital system. The hospital system will meet standards set out by various federal bodies for healthcare programs to increase the use and accessibility of healthcare technologies by ensuring the process’s baseline.
Data architectural models
Data processing describes the data, as well as the schemas, integration, transformations, storage, and workflow needed to allow the information architecture’s analytical requirements” (Sherman, 2015). It is critical to have a data architecture in place because it determines how data is processed, integrated, stored, and distributed to the appropriate location. It also ensures that the data is usable in a complete and correct format, allowing it to be used effectively. “Data architecture refers to a higher-level view of how an organization manages its data, such as how it is classified, integrated, and processed. “Data modeling” (Sherman, 2015) refers to “very precise and comprehensive rules on how pieces of data are organized in the database.”
As Compliance Manager, I will ensure that from Data processor ICD-9 CM code transfer correctly, all patient information like Identification, the name is accurate. In addition to that, I will also make sure that flow is precise from Provider transcribes treatment and diagnosis into the EHR system, NLP processes, and CAA working properly for billing and reimbursement.
Data architectural models (Denzer et al., 2002)
Natural Language Text Processing (NLP)
The program Natural Language Processing (NLP) transforms unstructured data into structured data. NLP uses artificial intelligence to process computer-human language interactions, allowing computers to process and interpret large amounts of natural language data. By extracting data from free text, providers use natural language processing (NLP) to boost health results and simplify the data entry process. Clinical notes and narratives are converted into a structured format using this helpful method.
In the last decade, speech recognition (VR) and electronic health records (EHRs) have also become popular in medicine (Hoyt & Yoshihashi, 2010). Due to the data entry burden, the EHR adoption rate is reported low. With the development of voice recognition technologies in terms of speed and precision, it could be possible to improve the process of entering data into electronic health records and reduce one of the significant barriers to EHR adoption. The use of voice recognition software in electronic health records will help healthcare facilities implement the system by which the pace at which data can be entered and enhancing the accuracy of the notes.
The conversion of hard copies of patients’ medical records to electronic files stored in a digital networking system is known as document imaging. This allows healthcare facilities to scan their old documents and upload them to an online system, which then allows them to build and maintain new records electronically.
Clinical Data Standards Theory and Development
Clinical data standards are an essential component in sharing accurate clinical data between information systems (The American Health Information Management Association, 2018). These specifications specify what information/data must be compiled, how the collected information must be interpreted, and how the data must be configured for data sharing internally and externally. Before introducing the electronic health record and health information exchange framework that supports the interoperability application that needs to be implemented into the system, these standards must be developed and circulated within the healthcare systems for proper use. The networking, Interoperability, and data sharing will all be supported by this platform. Electronic health information interoperability has been broken down into four standards such as Contentstandard, Transport standard, Terminology/Vocabulary, privacy, and security standard (Healthcare Information and Management Systems Society, 2021). The structure and arrangement of electronic message content and standard data sets for message forms. The format of messages sent between computer systems, paper design, clinical models, user interface, and patient data linkage are examples of transportation. Effective communication is the capacity of a sender and receiver of information to represent concepts clearly and precisely.To safeguard an individual’s or organization’s right to control what, when, by whom, and for what reason their protected health information is obtained, accessed, used, or released. Safety refers to collecting administrative, physical, and technical steps taken to safeguard the confidentiality, availability, and integrity of health data.
The primary aim of a strategy for data integration is to ensure that data can flow between the two separate systems safely and efficiently (Giannangelo, 2019). Data integration can be described as “the process of connecting various data/data points from various sources into a single unified collection.” For accurate analysis, data is cleansed, interacted with, and converted between two or more applications during this process” (Singh, 2020). Data integration is critical in the healthcare industry because it facilitates Interoperability and improves patient care quality. If it is processed, stored, handled, and exchanged so that employees can locate, use, and interpret it, it may be the most valuable business asset. A data integration strategy is needed to achieve this.
As HIM Compliance manager for the hospital, I must follow up on data quality and safety standards. I will follow data governance, data standardization, data capture validation, maintenance and data capture, analysis, and outputs for data integration strategy (The American Health Information Management Association, 2018). Data governance refers to collecting policies and procedures that regulate who, how, and why data is handled within a corporation (The American Health Information Management Association, 2018). Strong data governance helps with enforcement and legal efforts by organizing data for retrieval and retention, especially over time. Data governance includes data modeling, data mapping, data audit, data quality controls, data quality management, data architecture, and data dictionaries (Giannangelo, 2019). It is crucial to have a data dictionary standardization in place to collect and record data consistently. The Standardized Patient Discharge Data Set (UHDDS), which consists of core data for hospital reporting, is one of the data sets used in the hospital environment. As an HIM Compliance officer, it has been a top priority to ensure that the hospital complied with UHDDS and followed the ICD-9-CM code.
The data format is critical for Interoperability, as it not only improves integration and Interoperability between departments/ facilities and ensures enhanced patient care and more accessible data access for analysis (Giannangelo, 2019). Using the most commonly recognized and used data formats in the healthcare industry will ensure that the company has good Interoperability and integration between all departments in the hospital.
Interoperability is the ability of various information systems, devices, or applications to communicate in a coordinated manner within and across organizational boundaries to access, share, and cooperatively use data among stakeholders to improve individual and population health (Giannangelo, 2019).
According to Becker’s Healthcare (2012), steps hospitals and healthcare systems take to achieve Interoperability, such as becoming a meaningful user, understanding Interoperability, following industry-standard, defining the information to be exchanged, and securing data to test the system. When it comes to data exchange, the healthcare facility must decide what sort of information they will share, such as clinical or nonclinical data, and what they will do about it. Following an industry-wide set of standards that govern how information is exchanged is one of the keys to effective Interoperability (Becker’s Healthcare,2012). There are four levels of Interoperability as Foundation, Structure, Semantic, and Organizational (Healthcare Information and Management Systems Society,2021).
Adding Interoperability to current electronic health records would boost patient safety while significantly lowering administrative costs. It will also allow clinical personnel to spend more time with patients because data to be shared across facilities and service lines once policies and procedures are in place that defines the criteria and permissions for sharing data.
Decision Support Systems
Clinical decision support systems (CDSS) are software programs that evaluate data to help healthcare professionals and clinicians make better decisions about their patient’s care and outcomes (Agency for Healthcare Research and Quality, 2019). CDSS is a type of decision support system (DSS). This application compiles raw healthcare data and documentation to help clinicians identify and solve issues, thus improving their ability to make informed decisions about patient care and treatment plans for all service lines. DSS will also minimize costs and improve patient satisfaction by reducing human errors and adverse effects. CDS can be used on various platforms (such as the Internet, personal computers, electronic medical record networks, handheld devices, or written materials (Agency for Healthcare Research and Quality, 2019). “Planning for a new health information technology (IT) system to support electronically-based CDS includes some key steps that need to take. Such as identifying the needs of users and what the system is expected to do, deciding whether to purchase a commercial system or build the system, designing the system for a clinic’s specific needs, planning the implementation process, and determining how to evaluate how well the system has addressed the identified needs”. (Agency for Healthcare Research and Quality, 2019, para 3).
Some programs are struggling to keep up with the introduction of modern electronic health records and numerous interoperability technologies to support the systems (Talend, 2021), enforcing current electronic health records in healthcare systems. Legacy systems are operating systems and technology programs that are outdated. These programs are still in operation, but the application’s updates have expired. Many facilities will depend on legacy systems that use read-only data storage. For the company, these systems may be a financial and process burden. The manual nature of legacy system processes can lead to errors. Service disruption, security risk, and a lack of system support are possible threats to these systems. The explanations for a company’s continued use of a legacy system are numerous (Talend, 2021).
Investment: While maintaining a legacy system is costly over time, switching to a new system necessitates an initial financial and workforce investment.
Fear: Transitioning an entire business or even a single department to a new system is complex, and it can elicit some internal resistance.
Difficulty: Legacy software could have been written in an out-of-date programming language, making it difficult to find staff with the requisite expertise to migrate it. The system may have little documentation, and the original developers may have left the company.
The most important aspect of upgrading a legacy system is to safeguard any existing data. This process can only be accomplished by completing a successful data migration. For successful migration steps such as extracting the current data, transforming data to match the new formats, cleansing the data to address any quality issues, validating the data to make sure the move goes as planned, and loading the data into the new system (Talend, 2021).
Clinical Data and Process Modeling
Clinical data and processing modeling, such as Unified Modeling Language (UML) and Unified Process (UP), are essential aspects of the healthcare industry (Techopedia Inc, 2021). The UML is a collection of best engineering practices for modeling large and complex systems that have been shown to work. The UML is an integral component of object-oriented software creation and the software development process. To express the design of software projects, the UML mainly uses graphical notations (Techopedia Inc, 2021). The UML allows software developers to define, simulate, construct, and record software system objects. There are two main types of diagrams, and within those types, there are 14 different types to help model software behaviors, program architectures, and workflows. Structure Diagrams and Behavioral Diagrams are the two most common forms.
Structure diagrams are involved in the system being modeled and used to record software systems’ design. Structure diagram’s example such as Class Diagram, Component Diagram, Deployment Diagram, Composite Structure Diagram, Object Diagram, Package Diagram (Techopedia Inc, 2021). Behavioral Diagrams display the interaction of the various objects in how they integrate and work together. Behavioral diagram’s example as Use diagram, Activity Diagram, UML state Machine Diagram, Sequence Diagram, Communication Diagram, Interaction Overview Diagram and Timing Diagram (Techopedia Inc, 2021).
Unified Process (UP)
Unified process (UP) is a system process engineering metamodel-compliant architecture-centric, use-case oriented, iterative, and incremental development process that uses the suitable modeling language (Techopedia Inc, 2021). Across contexts and organizational traditions, the appropriate method can be extended to various software systems of varying degrees of technological and managerial sophistication (Techopedia Inc, 2021).
Unified Process (UP) is a framework for UML. In software engineering, the UML includes a single process UP. Unified process (UP) is a system process engineering metamodel-compliantarchitecture-centric, use-case oriented, iterative, and incremental development process that uses the suitable modeling language. The UP can use for various software systems with varying degrees of technological and managerial sophistication. The appropriate software development process is another name for the correct process. With the UP, you can configure the system for specific projects. Business modeling, research, design, implementation, testing, and deployment are all part of the unified process’s four growth phases. The stages are as follows:
- Inception: This step provides an overview of the project’s scope and a high-level understanding of system architecture and specifications. I am also convincing stakeholders to support or oppose the project (Pearson Education, 2001).
- Elaboration: Defining project costs and establishing project schedules for status and projected completions by minimizing risk. This step introduces the development of system architecture design, implementation, testing, and baseline. At this stage, any potential hazards will be detected and analyzed to determine if they can eliminate from the project (Pearson Education, 2001).
- Construction: The software architecture progresses from building to the operating environment (release). The first models are intended to be used as research environments. The method moves on to the next step after the knowledge has been checked and validated to function correctly (Pearson Education, 2001).
- Transition: Ensure that the program meets the needs of the end-users by thoroughly testing it before delivering it to them. Minor changes need to be made after the release if bugs are discovered. The primary focus of user reviews will be on corrections, fine-tuning, configuring, updating, and other problems (Pearson Education, 2001).
Enable decision-makers to use data.
Data-driven decision-making (DDDM) is a method of gathering information based on observable objectives, evaluating trends and evidence from these findings, and implementing policies and practices that support the company in various ways (Durcevic, 2019). Rather than relying on guesswork, data-driven decision-making involves achieving key business objectives using checked, evaluated data. To gain actual value from your data, it must be both reliable and relevant to your goals. For improved data-driven decision-making in business, it was a massive task that inevitably slowed down the entire data decision-making process. Data scientists mine two forms of gold: qualitative and quantitative, both of which are essential for making a data-driven decision.
Interviews, photographs, and anecdotes are examples of qualitative data that aren’t described through statistics or indicators (Durcevic, 2019). Observation rather than calculation is used in qualitative data analysis. It is important to code the data, in this case, to ensure that objects are grouped systematically and intelligently. The emphasis of quantitative data analysis is numbers and statistics. The median, standard deviation, and other descriptive statistics are significant in this case. Rather than being studied, this form of study is calculated. Both qualitative data and quantitative data must consider making an informed data-driven business decision.
Enterprise-wide policy and procedure
The ZZZ Health care system recently adds quite a few new applications to the Electronic Health Record (EHR) system. The applications that we have been able to implement our Computer-Assisted Coding, where Natural Language Text Processing (NLP) will be used. The ZZZ hospital will also be using voice recognition along with some data capture applications, which will provide the capability of document imaging. An enterprise-wide policy is required to ensure that all employees consistently perform their duties, standard protocols and procedures help keep things going smoothly, and staff can know how to manage various job situations.
|Title: Data Management (Data collection, storage and Maintenance)||Organization: ZZZ Hospital|
|Owner: Alpa Vashi||Version# 1|
|Effective date: 3/29/21||Revise /Retired date:|
|Approver: Privacy & Security Officer||Pages|
The ZZZ Hospital implements appropriate physical, technical, administrative, and operational security controls to protect ZZZ Hospital information, computing system/device, medical devices, and electronic media from unauthorized access, theft, damage, or destruction. These security controls are implemented per applicable law, accreditation requirement, and business practice.
To describe the appropriate use of hospital Data, information, networks, medical and computing systems/devices, and electronic media necessary administrative, technical, an operational safeguard to protect the confidentiality, integrity and availability of ZZZ Hospital’s information that are created, stored, processed or transmitted by ZZZ hospital.
This policy applies to all departments and staff from ZZZ Hospital, project contractors, and vendors.
Corrective / Disciplinary action:
Following the applicable policy of ZZZ Hospital, applies corrective/disciplinary action against individuals found, after an investigation by their respective employer or contracting party, to violate this data management (collection, use and maintenance) policy.
- Data collection
- Personal health data should be obtained only by fair and lawful means, and, if applicable, with the knowledge or consent of the pertinent individual
- Data should be obtained only for specific, lawful purposes.
- Data must be accurate and up to date and must be readily available.
- Data capture, validation, and processing should be automated wherever possible.
- Data storage
- Data must protect by law, contractual agreement, or business stipulations.
- All data must be restricted and only access to member based on requirement of their job function.
- Data encryption mechanism to secure data must apply.
- Data back must complete daily basis in midnight.
- All staff to utilize their badge access while working in a physical building.
- Need to secure all laptops and electronic devices with a strong password and assign tag number.
- External detachable storage e.g., any USB attachable storage devices Encryption required.
- Maintenance of health care data
- Departments must take Inventory of data and action to prevent data loss.
- Understand data sensitivity.
- Follow record retention policies and procedure.
- ZZZ Hospital implement reasonable measures to detect and prevent unauthorized changes to hardware and software.
- Departments must take action to prevent data loss.
- Updating and maintaining Confidential /restricted information data flow into and out of ZZZ hospital.
- Establishing data protect practice like document shredding, secure lock.
Agency for Healthcare Research and Quality, na. (2019, June). Clinical Decision Support. AHRQ. https://www.ahrq.gov/cpi/about/otherwebsites/clinical-decision-support/index.html
Denzer, R., Güttler, R., Schlobinsk, S., & Williams, J. (2002, May). A Decision Support System for Marine Applications. https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.533.5829&rep=rep1&type=pdf.
Durcevic, S. (2019, April 16). Data Driven Decision Making – See 10 Tips For Your Business Success. datapine. https://www.datapine.com/blog/data-driven-decision-making-in-businesses/.
Giannangelo, K. (2019). Healthcare Code Sets, Clinical Terminologies, And Classification Systems (fourth). AHIMA
Healthcare Information and Management Systems Society, , I. N. C. (2021, February 24). Interoperability in Healthcare. HIMSS. https://www.himss.org/resources/interoperability-healthcare
Hoyt, R., & Yoshihashi, A. (2010). Lessons Learned from Implementation of Voice Recognition for Documentation. AHIMA foundation. https://perspectives.ahima.org/lessons-learned-from-implementation-of-voice-recognition-for-documentation/.
Pearlman, S. (2019, October 23). How to develop the right data integration strategy for your organization: Talend Blog. Talend Real-Time Open-Source Data Integration Software. https://www.talend.com/blog/2019/10/23/develop-data-integration-strategy-for-organization
Pearson Education, , I. (2001). Overview of the Unified Process. InformIT. https://www.informit.com/articles/article.aspx?p=24671&seqNum
Singh, A. (2020, June 26). Data Integration in the Healthcare Industry: How iPaaS is Revolutionizing the Healthcare Sector. APPSeCONNECT. https://www.appseconnect.com/data-integration-in-the-healthcare-industry-how-ipaas-is-revolutionizing-the-healthcare-sector
Sherman, R. (2015). Data architecture. Science Direct. Retrieved from
Talend, na. (2021, January 13). What is a Legacy System? – Talend. Talend Real-Time Open-Source Data Integration Software. https://www.talend.com/resources/what-is-legacy-system
Techopedia Inc, na. (2021). Unified Modeling Language (UML) What does Unified Modeling Language (UML) mean? Techopedia.com. https://www.techopedia.com/definition/3243/unified-modeling-language-uml.
Techopedia Inc, na. (2021). Unified Process (UP) What does Unified Process (UP) mean? Techopedia.com. https://www.techopedia.com/definition/3885/unified-process-up.
The American Health Information Management Association, na. (2018). Driving Compliance through Data Governance. Journal of AHIMA. http://library.ahima.org/doc?oid=89117#.YF-YO69KhPY
The American Health Information Management Association, na. (2018). HIM Functions in Healthcare Quality and Patient Safety. Journal of AHIMA. https://library.ahima.org/doc?oid=104841#.YF-UAK9KhPY
The American Health Information Management Association, na. (2018). Data Standard Time: Data Content Standardization and the HIM Role. Journal of AHIMA. https://library.ahima.org/doc?oid=65894#.YGT8mK9KhPY
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