Health information exchange (HIE) refers to the mobilization of the health care information electronically across companies within an area, hospital, or community system (Blavin, Devers & Ramos, 2014). It also provides the ability to shift clinical data electronically among distinct health care information systems. This paper will look at the different HIE models available, focusing on the Florida HIE. It will discuss the services offered by the HIE and its contribution to information operability and information exchange. The paper will also elaborate on data integrity, and policy and procedure development. A conclusion will be provided at the end.
HIE Models
Some of the common HIE models that are presently being used throughout the nation include the centralized/consolidate, decentralized/federated/distributed, and hybrid models (Abramson, Silver & Kaushal, 2014). Under the centralized HIE model, all information is kept in one warehouse or data repository and individuals often hand over patient information while being able to see it through exterior delivery techniques (Abramson, Silver & Kaushal, 2014). In the decentralized model, all information remains at the Point of Service (POS) and the participants are members of a company who accept to share their data with others of the same company (Dixon, 2016). On the other hand, the hybrid is a combination of the aforementioned models, and none are prominent architecture.
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The centralized model eases querying data and works effectively in community networks. It also assists in community-wide data evaluation for research and regional public health problems (Blavin et al., 2014). Contrarily, the model relies on the individuals to hand over data, making it possible for data duplication from many providers to occur. The decentralized HIE model is useful in guaranteeing that the whole framework does not collapse if one health care provider has technical hardships (Dixon, 2016). On the other hand, it presents hardships in setting up and maintaining a far-flung, interlinked web of providers with many points of connection.
The hybrid model facilitates clinical analytics, medical study, and risk stratification by developing patient cohorts via the data warehouse (Abramson, Silver, & Kaushal, 2014). Contrarily, it does not address the issue of the high expense of a central database.
Florida HIEs
The Florida Health Information Exchange services are changing health care in this particular state through utilization of health information technology (Dixon, 2016). Florida offers services which allow for the safe electronic exchange of patient health data among providers of health care (Blavin et al., 2014). Three services offered by the Florida HIEs are Direct Messaging, Query Solutions, and Encounter Notification Service.
The Direct messaging services provides healthcare firms and providers with a way to safely relay health information over the Internet (Abramson, Silver & Kaushal, 2014). It provides the most superior level of privacy, protection, and interoperability. The Direct Messaging is nationally recognized via Direct Trust and allows for HIPAA-compliant, encrypted relaying of secured health data. It can be used to attain significant utilization by enabling the electronic exchange of clinical data. Query Solutions facilitates connectivity on the health exchange, both statewide and nationally (Blavin et al., 2014). This is the act of looking for clinical documentation for a particular patient across a single or numerous clinical information sources.
Notably, the connectivity represents four federal entities, 50% of American hospitals, 8,300 pharmacies, 26,000 medical groups, and 3,400 dialysis centres (Abramson, Silver, & Kaushal, 2014). Encounter Notification Service provides users with timely notifications regarding their member's hospital experiences (Blavin et al., 2014). This particular service offers the chance for users to better participate in care coordination, ensuring that correct follow up care is received Data Integrity and Data Quality
Data integrity is the fact that information must be reliable and correct over its whole lifecycle (Dixon, 2016). It is also an important aspect to the design, implementation, and utilization of any given system which keeps, processes, or acquires information. Data integrity is basically the opposite of data corruption (Blavin, Devers, Shah & Ramos, 2014). People tend to confuse data integrity with data security, which is the discipline of safeguarding information from restricted users.
Data quality, on the other hand, is the wholeness, correctness, consistent, and timeless state of data controlled in a firm's data warehouse (Dixon, 2016). It is considered to be the basis and/or building blocks that create data integrity. Two ways that one could test the integrity of the data being exchanged between the hospital and state's HIE to ensure data quality would be to validate the data and eliminate duplicate data (Blavin, Devers, Shah & Ramos, 2014).
Sensitive information from a safe database can easily find a place on a given document, email, shared folders, or spreadsheets where workers without authorization can see it. It is, therefore, crucial that one cleans up stray data and eliminates duplicates (Abramson, Silver & Kaushal, 2014). On the other hand, it is important to certify that information processes have not been compromised. Prior to validating information, one could identify the main attributes and specifications that are vital to the company.
Policy and Procedure Development
Today's health care organizations must navigate a rather complicated array of federal and state privacy and safety laws to develop policy, which in turn establishes a complicated environment for instituting trust (Dixon, 2016). Based on the interior necessities and how laws are interpreted as well as identified, most organizations tend to take different approaches to developing security and privacy policies. The consequential variation presents a challenge to broad HIE and that is why it is crucial for organizations to agree on a common set of shared policies (Blavin et al., 2014). As recently discovered, HIE is safeguarded by a patchwork of policies, state laws, and practices that has evolved with time, state by state, and organization by organization, without an all-inclusive approach or plan (Abramson, Silver & Kaushal, 2014). Furthermore, a majority of these laws were meant for paper-based systems, having failed to expect electronic HIEs.
Two operational policies and procedures that hospital staff would need to develop in order to legally participate in the HIE are the opt-in and opt-out policies (Dixon, 2016). Under the opt-out policy, individuals are automatically enrolled in the HIE, but are provided with a chance to opt out of having their information released/kept by the HIE (Blavin et al., 2014). On the other hand, the opt-in policy requires staff and patient permission in order for patient health information to be kept and/or released by the HIE.
References
Abramson, E.L., Silver, M, & Kaushal, R. (2014). 'Meaningful use status participation in health information exchange among New York state hospitals: A longitudinal assessment.' Joint Commission Journal on Quality and Patient Safety, 40(10). Pp. 452 - 460.
Blavin, F., Devers, K.J., & Ramos, C. (2014). 'How local context affects providers' adoption and use of interoperable health information technology: Case study evidence from four communities in 2013.' Issue Briefs, Urban Institute.
Dixon, B. (2016). Health information exchange: Navigating and managing a network of health information systems. New York: Elsevier Science.
