After reading the assigned materials/ links and going through

Jenny

I think personal and confidential  stored data/information is important during a healthcare visit. Personal as in the reason you are having an appointment or stuff that people don't need to know except the healthcare provider. Confidential as in information that cannot be leaked other than to who you or the patient ok'd for, like a home address or phone number is used to have a backup option to communicate. 

There are pros and cons for using physical and electronic data. Physical data ca be damaged or stolen physically like water leak, in person robbery, etc. Meanwhile, electronic data can be hacked into, maybe lost due to loss of power, it can be corrupted. It also cost more to do electronic to make and use, while it is easier to input and store data easily.

Thomas

Data is stored within the electronic health record in a healthcare setting.  It Is stored either through physical data or electronically via a database.  Each system has its own standards of how to organize the data in the database.  One of the ways is through master data management.  According to Harman et al., the data in the medical record allows us to use it as communication tool to help guide us as providers to make a clinical decisions for a patient's treatment.  It also allows us to coordinate services with other specialties, assist with research, and also provides us education on new developments in our healthcare.

There is a difference between storing physical data and electronic data.  When storing physical data, there is a lack of security. Security also occurs with electronic data, however it is different in the sense that the concern is more data can be hacked, manipulated, or destroyed, as described by Harman et al. The concern with physical data is that access is controlled by locks and safes, and only by authorized users.  Electronic data can be tracked by audit trails by generating date and time-stamps, and detailed listings.  Physical data can only be tracked if the user themselves documented properly.

References:

Harman, L., Flite, C., & Bond, K. (2012 September). Health information Systems: Past and Present.  American Medical Association Journal of Ethics. 14(9): 712-719.

Sophia

How is data stored and what function(s) does it provide in a healthcare setting?  

Data is stored in specific structured or unstructured databases that are all connected to make one master data system. For example, x-ray images are unstructured data that's stored in specific databases and use meta-data to differentiate and identify them so they may be accessed when needed. Most data in healthcare is unstructured. Data that is structured, such as patient information, is stored in a relational database and is mapped with other databases. 

The data storage method serves to ensure that all data is properly connected, accessible, redundant and secured so healthcare facilities have a convenient, cost effective and safe way to store and retrieve healthcare data.

Storing physical and electronic data differ in storage location, accessibility, security and redundancy. Physical data is safer in some respects, since it's impossible to hack paper, but they aren't as accessible. They also can't be updated as easily/frequently in many situations. 

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1 This course reviews the topics that are included in the concepts and skills in understanding and managing healthcare data standards and interoperability.

2 The way that data standards are maintained and sustained within the electronic health record is how the data is mapped and stored. Data architecture is the foundation of the specification and requirements that are needed for data modeling. Where the data is stored and how it’s arranged builds the strategy for creating good data standards and allows interoperability. This module looks at the approach to creating standards from modeling and design to mapping, leveraging master data management and reference files, master files, and dictionary files to allow data to be used across healthcare information technology. This module also covers how data mapping and modeling impact security, life cycle, and disaster recovery and how the approaches to metadata are used to organize data for data mining.

3 The objectives in this module focus on data standards and how storage, mapping, routing, and how the data is arranged, impact interoperability and integration with healthcare information technology. The role of data architecture through modeling and mapping becomes the foundation for many of the functions in the EHR, both for the healthcare of patients and system operations.

4 One of the biggest impacts on data standards in healthcare information systems is the system architecture. The architecture is a master plan of the data, including the physical and electronic layout and rules that govern the flow of data within a system. When electronic health records first started, the scope of system architecture was confined to a simple database that included orders and results. The scope has increased so that several applications and databases support CPOE, computerized physician order entry. Adding patient information management, revenue management, ambulatory scheduling, ancillary systems, and hospital operations, has created a diverse and complicated architecture of the healthcare enterprise. The first step in architecture is modeling, understanding the data value including type, use, and flow within the healthcare system. Since most healthcare information systems are an aggregation of many smaller systems, data modeling becomes increasingly important in managing this complexity to maintain standardization. It is this standardization that allows integration between system and healthcare interoperability to occur, no matter the secured format. A data architect is a role that manages the master plan, but this role is divided between individuals and departments, some of which never communicate with each other. We find that much of the modeling and design of the enterprise architecture is established in the individual application and programs that make up the healthcare information system. Each different system has its ways of managing healthcare information and has its own rules for organizing data in a database. For each program, the architect is the healthcare application vendor. The vendor will define how the data will be stored in their application, however in some cases, the healthcare organization has some control over the type of data being stored. For example, one of the fields in an EHR is weight. This can be expressed in pounds or kilos, depending on what the organization decides is the standard. In addition to the programmatic data master plan, there also needs to be a data plan on how data is shared between applications and across physical devices. Weight may be information for the lab information system and the master plan sharing the weight between the EHR and LIS needs to be defined. This is typically part of the system operations department as this would be passed from one system to another by interface, network, or internet routing. The complexity of defining standards by creating and managing a healthcare enterprise architecture is complex and vital and must always be done in a secure exchange format such as HL7 or FHIR.

5 One of the ways to manage data standards is through master data management. Master data management is identifying identity data and referent data to create a single consistent point of reference. When we reviewed the electronic medical records, the typical point of reference for patient identity is the medical record number. Some organizations have variations on this name, but all have a single point of reference that all the demographic data links to. There are rules and standards regarding this number, such as numeric or alphanumeric, acceptable length, or the presences of a check digit but all applications that use this as a standard have rules that maintain the integrity of this standard. Also, the concept that is included in identity data and referent data is the use of dictionary tables or lookup tables to maintain data standards. Before entering a new patient, staff should search for them by date of birth and other identifying factors and compare possible matches to avoid 2 records for 1 patient. Duplicate patients can create fraud and abuse situations, inaccurate patient reporting, and other medical authentication concerns within a practice. An example of a data dictionary in the electronic health record is the chargemaster. The chargemaster is a database that includes all relevant data about all hospital charges including insurance and employer information and may be linked to other tables such as order entry or medication master file. Chargemasters are consistently priced, and may include allowables per payer contracts to help organizations and posters know what the appropriate amount to collect would be. This allows the system to have a code or value that is linked to a set of other standard values so that data integrity is maintained. The role of master data management becomes an important rule that governs data management. Another type of data that is critical to establish data standards within a healthcare system is metadata management. Metadata is data about data and is being used increasingly in data analytics and reporting. One way that metadata is used is to monitor activities and timestamps for nursing management. In the days of paper records, nurses were audited by the nurse manager to verify they documented patient progress at defined time intervals. Now electronic health records include a timestamp when clinical documentation occurs, and this metadata can be used to programmatically to audit nursing and physician chart documentation. Metadata is also added to data when extracted to a data warehouse to allow cataloging and indexing of data. This includes when a claim is released to the clearinghouse and payer, and when a response and payment is returned. Interoperability is using more metadata and standards to define metadata is becoming a great part of the data architecture. This means that managing data architecture is becoming more complex and the rules to set up new or modified applications need a process to manage this outcome. Data governance is the procedures and policies that maintain and enforce data standards with the healthcare information system. The best data governance for enterprise architecture has a couple of characteristics. It includes all departments that interact with the data and follows a defined set of rules for data integrity across the system. When approved and implemented, an effective enterprise architecture governance documents the new architecture. The goal of maintaining standards is interoperability and integration. Interoperability is the ability of applications and programs to share information across the system and integration is being able to combine information from many sources into one unified view. Both interoperability and integration require data standardization to work.

6 HITECH was created in 2009 to further secure patient information methods and encouraged the use of EHRs. With this in mind, data architecture is possible and is modeling healthcare information. There are three different views or data models. Each model represents how the data is stored and the relationship between data items. Each model has a specific purpose. The conceptual model defines what the system contains. For example, a radiology information system contains textual patient data and radiology picture files. The logical model includes how the system is implemented and the data stored within its application. This includes the programs and rules to use and create data standards within the radiology system. The physical data model is the models of how the information is shared with the other system resources. The physical model would include the method of connection and routing for a radiological picture to be shared and viewed within a CPOE system. This means that different maps get created for each application and a system-wide map gets created that shows the flow and traffic through the network, from the source to the destinations. Standards in mapping may include networking within an organization of electronic data interchange between organizations. These connections all need to be defined to establish standards from one system to another and sometimes involve the transformation of data between systems, a process typically found in interface engines. The security of these methods follow the CIA triad- Confidentiality, Integrity and Availability. During this process, the network map of the data location and flow needs to be built and maintained by the data architect to evaluate new applications or programs, or by data governance for auditing. There are software programs that can create data maps, based on maps or schema produced by vendor or network engineers. Some of these software programs can also create a data map based on system activity and can be used to monitor system performance.

7 Two factors that define the data mapping is where the data is stored and how it is organized in the application or program. Some simple programs use flat files to store information. A flat file is a simple text file that contains only numbers, letters, or symbols and does not contain information about other files. A common flat file in healthcare is called an ASCII file. ASCII stands for American Standard Code for Information Interchange where each 7-bit binary combination stands for a letter, number, or symbol and is the basis for a text file. A tree file or hierarchical file had a parent file with multiple children files; however, it is more common to find a relational database where the values of one table can join to a value on another table. The relational database management system is more common and relies on structured values. Relational databases in healthcare created a means to provide improved data analytics but the integration between databases meant the data values had to be standardized. For example, if the radiology database could create medical record numbers and the electronic health records also created medical record numbers, there was the danger of a mismatch of patient to their specific healthcare data. If the radiology system creating a 10-digit number and the EHR only stored 8-digit numbers, this would also be a problem. Using master data management as a way to identify and enforce data standards required understanding the data needed in each database. Remember, not all healthcare data is structured data. Recall the video about structured and unstructured data. Pictures, documents, images, graphs, and transcribed text are all examples of unstructured data. One approach to handle this was a non-relational database to link non-tabular data to other data. Approaches that link documents, graphs, and key pairs are ways that databases can be used to organize healthcare data. Another way to organize data is to extract it to a data warehouse. Data warehouses can be a specific subset of the data found in an EHR and can be transformed to manage data standards. Patient data that can be linked back to a patient include their insurance policy information, phone number, identifying patient information, charges per visit, orders and results for imaging and labs, and many others. Another use for a data warehouse is to add metadata to be able to analyze the data that is extracted. Data architecture and mapping are important to create network and server strategies for system performance. Many practices utilize a PM software on their network, but it is linked to a server that they may or may not own. If a healthcare practice is using another company’s server, is important to make sure their data is secure, and it is on the vendor to have enough private server capacity for the different client’s capacity that is needed. The physical data map shows the flow of data between applications and programs and allows the network engineers to define the best path of data flow. The goal of network engineers is to increase the speed of transmission of data packets and reduce the number of data collisions. This can be done by static or dynamic routing. Static routing is to set up the routing of data between applications to take one defined pathway. The advantage is the data packets stick to their path and cause less traffic and data collision in other parts of the system. The downside is that a static system can't take a less used route, it has to stick to the defined path. A system with dynamic routing will take different routes to get to the target program. Dynamic routing can avoid traffic backup but may take a longer route to get there. This balance between static and dynamic routing is visualized through data modeling and mapping and is important to understand to reach desired results.

8 Some of the processes in system operations affect data standards. Three areas that are impacted by data standards are security, data lifecycle, and disaster recovery. Cybersecurity within an electronic health record is defined by HIPAA, which defines the standards that patient data must be protected for privacy, but also contain standards so that information can be transmitted between the organization, such as the hospital and Medicare for insurance reimbursement. The mapping in data architectures needs to be able to define the data types that are consistent between the healthcare organization and the insurance provider so that an 837 transaction, an interface-specific for sending hospitals, and reimburse data to replace paper forms matches the data between the sending and receiving systems. Another type of mapping that needs to be defined is the type of access and encryption to allow authorized users to access patient data. These functions are supported by data standards and modeling and mapping efforts of data architects. We had also covered the need for new programs and applications to have up to date data maps. These mapping programs can provide system performance metrics and can also manage the software data life cycle or SDLC. Programs or applications that are becoming out of date can be analyzed for ways to extend or replace functionality and produce a set of proposed requirements of application or program replacements. Another system operation function is disaster recovery, the activities to backup and restore vital system information. In the data center project, I worked on, we analyzed available data and the corresponding metadata for performance, frequency or review, and amount of updated information; I was able to propose disaster recovery processes for vital systems. Click the check mark to learn extended information on data standards.

9 The goal of using data standards is to allow interoperability, for the system to share information across the system. Sharing information allows the system not to have to write a whole record in multiple places but can use data from one source in another. Interoperability also works with master data management to maintain system integrity. If multiple sources of the same information are used, then it's possible to create an out of sync condition, where the same data source is represented by two different values. The architecture strategy to focus on one source of truth keeps the data consistent. The ability to integrate data is to use data from multiple sources to one combined view. Metadata is data that can be added to healthcare data to provide additional analytic capabilities. One of the uses of metadata is to manage "big data" or the overwhelming amount of data points that is common in many large-scale electronic health records. When there are too many data points, in some cases it's harder to analyze the data. Data mining is a technique to filter large collections of data to be able to analyze trends or patterns. By adding metadata programmatically to classes of information, techniques like indexing, sorting, and chunking can be used. An example is the age and/or DOB of the patient, time is a continuous type of data, but you wanted to analyze the data by categories, you could add metadata that created chunks, like boomers, generation x, and millennials, three ranges of age groups. You could do something similar with patient zip codes or the chief complaint/ reason for visit. These approaches to how data is stored and mapped allows data architects the ability to manage data using standards and can create important reporting capabilities to higher-level management when they are working on marketing campaigns or trying to gauge what type of employees are needed for their practice. Make sure you complete the readings for this week to understand data standards and interoperability.

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