There are various clinical coding and classification systems. Encoders and computer-assisted coding are two examples of such systems or applications (CAC). Yet, because of its increased speed and accuracy, the company should consider deploying a CAC system. The effective implementation of clinical documentation improvement (CDI) programs improves the accuracy of patients' clinical status representation. Yet, ensuring a high-quality CDI program presents a number of obstacles. The mismatch between physicians and specialized decision trees is one of these difficulties. Another issue is the misalignment of CDI specialists, coders, and reimbursement. A difficulty in the CDI process is the separation between queries and physician workflow. Patient matching is one of the interoperability issues within the health information exchange (HIE). One of the ways of addressing the patient matching issue is ensuring integrity in patient identification. It can also get addressed by developing policies that ensure the accuracy of the primary demographic data, as well as the outlining of the duplicate record validity procedures. Some of the applicable health information systems (HIS) include strategic or operational, clinical and Administrative, and Electronic Health Record (EHR) and Patient Health Record (PHR). However, it would be advisable for the organization to consider using Electronic Health Record (HER) system for disaster recovery purposes. Additionally, some of the appropriate data storage designs include cloud storage, Server-Based, and traditional storage. However, the use of cloud storage design would be the most appropriate for disaster recovery purposes.
One of the managerial challenges relating to databases, clinical indices, and registries in hospitals is insufficient data standards. Other challenges relate to variation in state privacy rules, as well as investment costs and financial concerns. Some of the best practices that the organization needs to adopt for effective management of secondary data sources include balanced and lean data governance, enhancement of data quality, and ensuring adequate data content. Bottom-up and top-down designs are some of the data warehouse design approaches that the organization should use so as to support quality management of data from different sources.
Introduction
As a Director of Health Information for a large hospital, I sit on various institution-wide committees that govern the organization's policies. The arising issues have made me propose various changes in policies, operations, and procedures across the hospital. This paper, therefore, seeks to present my developed proposal in collaboration with the committee teams a to the hospital's CEO and the Board of Directors regarding various arising issues. Such issues relate to the implementation and management of electronic applications and systems for clinical classification and coding, the accuracy of diagnostic and procedural coding, as well as information operability and exchange. Other proposed improvements relate to the health information systems and data storage design, transfer of data from various sources to create meaningful presentations, as well as the management of clinical indices, databases, and registries.
Evaluation, Implementation, and Management of Electronic Systems for Clinical Classification and Coding
Several applications or systems exist for clinical coding and classification. Some of such applications or systems include the use of encoders and computer assisted coding (CAC) (Newby, 2016). There is a need for the hospital’s CEO and the Board of Directors to consider one of such systems to enhance meaningful presentation as well as the management of the organization’s clinical indices, databases, and registries.
Despite encoders and computer-assisted coding being used in carrying out similar operations, the increased complexity of clinical coding in the hospital requires the CEO and the Board of Directors to consider embracing the use of one clinical classification and coding system that would enhance accuracy and efficiency. Such a move would ensure meaningful data presentation and effective management of the hospital's clinical indices, databases, and registries (Newby, 2016).
The computer-assisted coding (CAC) systems can combine codes. However, unlike the use of encoders, they are not enough to determine when they should not combine codes. Additionally, the CAC software can assume that when various specific terms such as COPD gets documented, the code that needs to get assigned is that which represents a specific condition. For example, 492.8 represents emphysema and not otherwise. Besides, CAC software can also make the assumption that other conditions like pulmonary fibrosis with code 515 can get coded with COPD (Welker, 2007).
On the other hand, the use of encoders allows coders to read the documentation for various conditions such as dehydration and make a review of the patterns of creatinine throughout the patient's stay so as to identify the undocumented conditions such as renal failure. As opposed to CAC software, if the encoders find insufficient documentation of acute renal failure by a physician, they will not assign code 593.9 automatically (Newby, 2016). Instead, they will have to take acute renal failure (code 584.9) into consideration and search for a particular hidden disease process. When the encoders analyze a chart, they can, for example, identify Graves' disease documentation in the medical history of the patient. Such analysis will indicate that maybe the patient does not have Graves' disease and that it is either subtotal or total thyroidectomy or post-radioiodine treatment (Newby, 2016).
Additionally, CAC encoders, for example, can determine the documentation of valvular heart disease in a patient’s record and lead users to endocarditis diagnosis, which is not what the practitioner intended to perform. That shows that the CAC software is dependent on encoders. However, the logic of encoders can lead to several errors that any vendor can be willing to disclose (Newby, 2016).
Despite the installation costs associated with the implementation of CAC software, its implementation is relatively easier compared to that for encoders and the organization's CEO and the Board of Directors should consider embracing the use CAC system in the hospital due to its higher accuracy and speed (Welker, 2007). Additionally, the use of CAC applications creates a stronger incentive towards improving productivity in clinical coding and classification processes. The adoption of the use of CAC applications in the hospital will therefore significantly enhance the organization’s coding processes without necessarily replacing the coding professionals at the hospital.
Evaluation of the Accuracy of Diagnostic and Procedural Coding
Clinical documentation is currently a fundamental requirement by every patient. However, meaningful clinical documentation needs to be timely, accurate, and reflect the scope of the provider services at the hospital. Effective implementation clinical documentation improvement (CDI) programs enhance accurate representation of the of the clinical status of the patients thereby translating into coded data (Pryadarsini, 2016). The coded data then gets translated into physician report cards, public health data, quality reporting, reimbursement, as well as disease trending and tracking. The convergence of clinical coding and documentation processes is critical to both a healthy patient and a healthy revenue cycle. CDI, therefore, creates a direct impact on the care of patients by giving information to all the care team members together with those practitioners who may be offering treatments to patients at a later date (Pryadarsini, 2016).
Ensuring a quality CDI program comes with various challenges and the greatest CDI challenges under ICD-10 relate to increasing clinical specificity as well as how to attain high granular levels of compliance with regards to clinical documentation. One of such challenges is the disconnect between the specificity decision trees and physicians. ICD-10 heightens the degree of pull and push between diagnosis specificity tools and EHRs within the coding software systems. At the same time, the decision tree tools entrenched in the coding software drill down to each diagnosis’ specifics (Stacy, 2014). The complications then occur when the physicians attempt to use the calculator correctly and succumb to selecting unspecified codes. Despite such a challenge, the use of specificity tools can provide correct documentation and coding. However, educating the physicians on how to learn and adopt such new tools is a slow process. It is, therefore, essential for the CDI teams to assist the physicians in navigating specificity calculators during the process of documentation and educate them on how to apply such tools (Stacy, 2014).
The second challenge is the CDI process is the disconnect between the CDI specialists, coders, and reimbursement. Healthcare systems, in general, must be in a position to identify their documentation and high-volume coding needs so as to approach the right professionals who can create a meaningful discussion for positive change (Pryadarsini, 2016). Traditionally, CDI specialists are known to be bridging the communication gap between coders and physicians. They are therefore in the right position to carry out that task and ensure effective implementation of CDI programs. When the clinical documentation specialists, coders, and revenue cycle engage in a collective discussion regarding various concerns and the necessary changes, the entire CDI process can become more accurate (Priyadarsini, 2016).
The third challenge in the CDI process is the disconnect between queries and physician workflow. Sometimes, there exists a disconnect between how hospitals and physicians perceive or view clinical documentation. The realignment of the workflows between the physicians and the hospitals, therefore, becomes necessary to ensure that the two parties are on the same page (Stacy, 2014). However, such realignment requires education and communication. The hospitals and physicians must, therefore, embrace communication to create a mutual understanding regarding diagnosis. Additionally, the success of CDI programs is heavily dependent on effective auditing as well as accurate procedural and diagnostic coding with classification systems (Stacy, 2014).
Information Operability and Information Exchange
Interoperability refers to the practice of accessing and sharing health information consistently, securely, timely, precisely, and appropriately. It makes it possible for the physicians to get the right information at the correct time for the right patients so as to make informed health-related decisions (Jacob, 2015). Several interoperability issues are possible within the health information exchange (HIE), and one of such issues is the accurate matching of patient's records. Patient matching is an interoperability issue because different systems employ the use of different demographic information in matching individual patients to their health records. Such practices may yield inaccurate results, since the patients may have similar ages, birth dates, and names (Jacob, 2015).
One of the ways to address the patient matching issue is to ensure integrity in patient identification. That would help in avoiding various mundane errors resulting from the patient’s birth year transpositions, misspelling or culturally recognized spellings of the last names of patients, and patients’ nicknames or their Social Security Numbers (SSNs). The elimination of such errors can lead to a successful linkage of patients’ electronic records across the organization’s administrative and clinical systems (Connoy, 2011).
The solution to the patient matching issue also requires organizations to develop various policies that can ensure the accuracy of primary demographic data as well as ensuring the use of such data in linking records across and within the electronic systems of health records. Additionally, such policies have to address the information accuracy at the initial capture point by the use of front-end verification, including quality monitoring by use of a duplicate creation rate as well as timely duplicate records correction (Connoy, 2011). However, the organizations should take care to ensure that the emphasis regarding the limitation of the duplicate medical record numbers (MRNs) does not cause a greater problem of a patient's registration to other person's MRN and merging the identities of the records of two different individuals. Additionally, organizations should ensure the validation of record-linking algorithm effectiveness before releasing records to a HIE or linking records within the organization (Jacob, 2015).
The patient matching issue can also get addressed by ensuring that organizations create outlines of duplicate record validity procedures and ensure that such procedures get followed accurately. Additionally, the organizations have to provide adequate staff training at every level so as to reinforce the significance of a successful Health Information Exchange (HIE) (Jacob, 2015).
Evaluation of Health Information Systems (HIS) and Data Storage Designs
Health Information Systems (HIS)
One of the health information systems (HIS) is Strategic or Operational Health Information System. The application of Strategic or Operational Health Information system forms one of the most commonly used methods for the classification of health information. The system has provisions for information systems dealing with specific information, and it has several advantages over other systems (Zhang, 2013). One of the benefits of Strategic or Operational Information system is that it allows for the assessment of an organization concerning the digitization spread in its information system. Additionally, it permits the identification of uneven or inappropriate development in information systems (Zhang, 2013).
Another type of Health Information System is the Clinical and Administrative Health Information Systems. Practically, it is impossible to create a clinical system which is independent of other forms of administrative data. Additionally, the system forms the foundation of an integrated health information system which is more of a master guide created around the basic administrative information regarding patients thereby providing links to various clinical systems (Welker, 2007).
The Patient Health Record (PHR) and Electronic Health Record (EHR) form another essential Division of Health Information Systems. The development of open PHR and EHR aims at fulfilling the need for an open and common standard in different countries. The design of pen EHR system aims at fulfilling the purpose of allowing health information system’s semantic interoperability between and within various EHR systems (Zhang, 2013). Additionally, all the Electronic Health Record systems exist in a non-proprietary format so as to prevent information lock-in by vendors (Zhang, 2013). From the discussed three Health Information Systems, the use of Patient Health Record (PHR) and Electronic Health Record (EHR) systems would the most appropriate for disaster recovery purposes due to automated information updates.
Data Storage Designs
One of the data storage designs is cloud storage. Cloud storage is a form of offsite computing that keeps data and allows for the access of such data at anytime and anywhere. With cloud data storage design, the end user does not need to make updates. Additionally, it enhances flexibility, automatic software updates, disaster recovery, and increased collaboration (Bermond, Jean-Marie, & Yu, 2015).
Another data storage design is the Server-Based or Hyper-Convergence, which involves the storage of data in individual servers at the data center. It has effective in-house control and speed. Additionally, it creates different servers with data stripped across them (Bermond, Jean-Marie, & Yu, 2015).
Traditional Storage System is another data storage design, which commonly applied as a cloud data storage backup. It only allows the access to stored information when one gets signed onto the internet connection in which it is stored. It is easy to manage and allows for storage upgrade (Bermond, Jean-Marie, & Yu, 2015). However, from the above three Data Storage designs, the use of cloud storage is the most appropriate for disaster recovery purposes due to its flexibility and ability to allow for automatic software updates.
Management of Clinical Indices, Databases, and Registries
One of the managerial challenges relating to databases, clinical indices, and registries, from the hospital health information's management function perspective, is insufficient data standards. While there exist various standards for exchanging information electronically among EHR systems, such standards are not sufficient to allow for the achievement of EHR interoperability (Agrawal & Nyamful, 2016). The foundation of a real data interoperability is the achievement of a broad data standards adoption. There is, therefore, a need for hospitals to focus on the creation of interoperable environment so as to benefit both the patients and the providers.
Another managerial challenge relating to databases, clinical indices, and registries is the variation in state privacy rules. The lawmakers could enhance effective management of databases, clinical indices, and registries in hospitals through the harmonization of privacy rules. The lack of harmonization of patient’s privacy rules makes it difficult for both the patients and providers to achieve the expected health information exchange benefits. Such a situation may leave most clinicians poorly equipped for the management of the patient’s behavioral and social health needs (Agrawal, Das, & Abbadi, 2012).
Investment costs and financial concerns stand as another managerial challenge related to databases, clinical indices, and registries in hospitals. The healthcare organizations that have spent several dollars on electronic health record systems without meeting the expected requirements for health data interoperability are reluctant in pouring more money into such projects. Additionally, the costs associated with the achievement of interoperability are significantly high (Agrawal, Das, & Abbadi, 2012).
One of the best practices that hospitals should use for the management of secondary data sources is the use of balanced and lean data governance. There is a need for the hospital’s Data Governance Committee to consider practicing a cultural philosophy that emphasizes data governance to the necessary level of achieving the highest common good (Agrawal & Nyamful, 2016). Another practice that can best ensure effective management of secondary data sources in hospitals is the enhancement of data quality. The hospital's CEO and the Board of Directors need to consider providing data quality as a means of achieving efficient and best management of secondary data sources (Agrawal & Nyamful, 2016). Ensuring adequate data content is another practice that hospitals need to consider to achieve effective management of secondary data sources. Such a practice would require a multi-year strategy for data acquisition and provisioning so as to expand the data environment (Agrawal & Nyamful, 2016).
Evaluation of Data from Varying Sources for the Creation of Meaningful Presentations
Bottom-up design and Top-down design are some of the approaches in data warehouse design aimed at supporting quality data management from different sources, data storage throughout the data warehouse model, as well as creating meaningful result into the presentation layer (Wen, 2014). The bottom-up data warehouse design approach involves the creation of data marts so as to provide reporting capability. The data marts then get integrated to develop a complete data warehouse and the implementation of data marts integration employs the use of data warehouse bus architecture. Since the data marts get created first, the generation of reports is a quick process (Wen, 2014).
In the top-down data warehouse design approach, the first step involves the building of data warehouse, after which, the data marts get created from the built data warehouse. Such an approach allows for consistent dimensional data views across different data marts since all the data marts get loaded from the data warehouse. The approach allows for easy creation of new data marts from the data warehouse (Jindal, 2012).
Conclusion
The achievement of effective Healthcare delivery requires proper implementation and adjustments of the right policies, procedures, and operations across the healthcare facility. It is, therefore, essential for the organization's CEO and the Board of Directors to consider implementing the proposed changes regarding health care policies, procedures, and operations so as to enhance the delivery of efficient and effective health services.
References
Agrawal, D., Das, S., & Abbadi, A. (2012). Data Management in the Cloud: Challenges and Opportunities. Synthesis Lectures On Data Management, 4(6), 1-138. http://dx.doi.org/10.2200/s00456ed1v01y201211dtm032
Agrawal, R. & Nyamful, C. (2016). Challenges of big data storage and management. Global Journal Of Information Technology, 6(1). http://dx.doi.org/10.18844/gjit.v6i1.383
Bermond, J., Jean-Marie, A., Mazauric, D., & Yu, J. (2015). Well-Balanced Designs for Data Placement. Journal Of Combinatorial Designs, 24(2), 55-76. http://dx.doi.org/10.1002/jcd.21506
Connoy, S. (2011). An Interpretive Study of Critical Issues in Electronic Health Information Exchange. International Journal Of Dependable And Trustworthy Information Systems, 2(1), 1-17. http://dx.doi.org/10.4018/jdtis.2011010101
Jacob, J. (2015). GAO Issues Report on Health Information Interoperability. JAMA, 314(18), 1906. http://dx.doi.org/10.1001/jama.2015.15025
Jindal, R. (2012). Comparative Study of Data Warehouse Design Approaches: A Survey. International Journal Of Database Management Systems, 4(1), 33-45. http://dx.doi.org/10.5121/ijdms.2012.4104
Newby, L. (2016). Reconciling Systems for Acute Myocardial Infarction Definition and Coding Classification. Clinical Chemistry, 63(1), 24-26. http://dx.doi.org/10.1373/clinchem.2016.266999
Priyadarsini, P. (2016). A Primer on Clinical Documentation Improvement Program. Health Care Academician Journal, 3(2), 74. http://dx.doi.org/10.5455/sad.13-1467646960
Stacy, T. (2014). The Impact of Electronic Health Record Documentation and Clinical Documentation Specialists on Case Mix Index: A Retrospective Study for Quality Improvement. Journal Of Health & Medical Informatics, 5(2). http://dx.doi.org/10.4172/2157-7420.1000154
Welker, J. (2007). Implementation of electronic data capture systems: Barriers and solutions. Contemporary Clinical Trials, 28(3), 329-336. http://dx.doi.org/10.1016/j.cct.2007.01.001
Wen, W. (2014). The design of Marine Data Warehouse ETL System. Applied Mechanics And Materials, 668-669, 1374-1377. http://dx.doi.org/10.4028/www.scientific.net/amm.668-669.1374
Zhang, Y. (2013). Welcome to Health Information Science and Systems. Health Information Science And Systems, 1(1), 1. http://dx.doi.org/10.1186/2047-2501-1-1
