Paper Sample on Database Technology: Healthcare Information Systems

Introduction

As the director of health information in this hospital, I am tasked with the governance of healthcare information storage and access has come to the notice of the management that issues are arising from the selection of storage of data, accountability, integrity, and transparency. This paper is a proposal to the CEO on the change of the information policies.

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Policies on Applications/systems for clinical classification and coding

Coding in the clinical field is very important, in the management of health information. It entails the description of injuries or illnesses and procedures, using numeric designations. The systems that are used are very critical because they determine the quality of the data stored and its security. The systems and applications should adhere to the principles of Health Information management (HIM).Clinical classifications are used by6 ever4y individuals in the healthcare system. The importance of these systems is that they help in the determination of consumption costs, options that can be used in the treatment of patients, and the outcomes. Two major systems can be appraised as far as accountability and transparency is concerned

(i)ICD-10-CM/PCS

This classification system is useful in information collection and research. It was initially used to make coding for deaths (Blash.etal, 2018). It was however recommended for other statistical reports due to its complexity. It is used to collect death statistics in global health care systems. There shall be more traini9ng needed on the coding processes when using this system due to its complexity. There is a need to incorporate an expert coder in the hospital, to conduct the training.

(ii)SNOMED CT

This system is important in the interoperation of the system. It entails a range of applications working together so that the health care workers can exchange information easily. The advantage of this system over the CD-10 is semantic interoperability, which allows the exchange of information easily and understandably, even when using other systems.

The SNOMED CT is a better system because it is automated and the occurrence of errors is unlikely it uses Natural language processing in various applications. The CD-10 system is prone to human errors.

Review of Principles and applications of classification systems used within the CDI program

ICD-10 is an example of principles and applications used in classification systems and medical recording auditing. The codes are more detailed and have been useful in the tracking of epidemics. Healthcare Common Procedure Coding System (HCPCS), is important in the tracking of supplies and healthcare products, not listed in the CPT codes. They are essential for Medicare and Medicaid procedures. Real-time reporting occurs at the CDI platform. The challenge with this platform is the gap between the terminologies used in the clinical setup with the coding terminologies by the healthcare workers. The other challenge is the hiring process for the right professional for the CDI. They should be aware of the guidelines of coding and compliance with issues surrounding healthcare provision. There should be an appraisal for constant training to enable workers to maintain accuracy during documentation for care during transitions.

Evaluation and appraisal of information management systems and Interoperability issues

Health Information Systems are systems designed to manage healthcare data. They organize patient's medical records, organize operational management, and support healthcare policy decisions. There are several types of Health Information Systems; Electronic Medical Records (EMR), Remote Patient Monitoring (RPM), and Master Patient Index.

A master patient index connects separate patient records across databases. The index has a record for each patient that is registered at a healthcare organization and indexes all other records for that patient. It is essential in reducing duplicate patient records that bring about inconveniency to the Healthcare Organization. Remote Patient Monitoring which is also known as telehealth monitors the patient's health such as checking blood sugar levels and blood pressure for patients with chronic illnesses. This system can greatly improve the emergency unit through reflexive responses crucial in saving lives. The Electronic Medical Record mainly records patient's data on their treatment and medical records and history. In regards to the way we store data, we could review these three alternatives for storing data; on-premises storage, on-cloud storage, and hybrid data cloud storage. On-premises storage involves building data centers in the hospital premises that do not require wireless connections to access. On-premises storage is secure as it does not use wireless connections, therefore, reducing the risk of being hacked. Another option is with cloud storage whereby information is stored online with built-in servers in the hospital premises. This option is flexible for patients who can access their records easily. Hybrid Data Cloud is almost similar to on cloud but the difference is that it uses different servers outside the hospital premises. Its advantage is that it does not occupy space on the hospital premises.

What I would prescribe is that we use in terms of disaster recovery purposes is that we use the Master Patient Index as our health information system and the Hybrid Data Cloud Storage. This is because both options use multiple servers and hence if one is attacked in cyber terms or through natural disasters the other servers would still work and store and provide information for our Healthcare Organization. Some of the challenges we face include; an unambiguous patient identification, Ensuring the safety of software in an interfaced, networked clinical environment, and developing proactive models, methods, and tools to enable risk assessment. What we could do regarding the policies related to the management of secondary data sources is that. First of all, we should data access processes to be discoverable and transparent for potential data users(Knowles.etal,2016). Secondly, we should establish clear penalties and sanctions for breaches of data-sharing rules. Thirdly and lastly, we should include data sharing and management plans in funding applications.

The interoperability of the healthcare information system is the ability of the system to work in unison, across the healthcare boundaries. The interoperability of the system is useful in the efficiency of care delivery. The issues with the interoperability of some systems like the CD-10 are the absence of semantic interoperability. The coding language is different from the one used by the physicians in real-life, requiring a lot of effort to decode the information. There should be plans to make the information a higher quality.

Data Warehousing

Data warehousing in the healthcare sector is an essential component, used in the analysis. It is the central point for all information regarding health care, from different multiple sources. The policies guiding the selection of the best model is essential because it determines the quality of information on cost accounting schemes, supply schemes, and other utilities. Proper warehouse design is also useful in the diagnosis, systematic, measure of diseases, and the management of delivery operations. The challenge with the current data warehouse is the difficulty of the warehouse operation team to maintain the current platform and also simultaneously develop fresh information in the constantly evolving system. The Enterprise data model method (EDM) (Cardoso.etal, 2017). This data warehousing design is essential in the collection of accurate raw data. It allows for an early determination of the goals that the healthcare system needs to achieve. It however has a major disadvantage in that the model tends to bind data at early stages, making it difficult for changes to be introduced. It has less consideration for the reality of any data collected in the organization, like updated costs of care per person for persons with malaria e.t.c.

This recommendation appraises the Independent Data Mart Approach model of warehousing design because it allows for the health care information management to build up information from small units. There is a space for the building of separate data mart for a single aspect such as costs or revenue analysis, making it easy to combine data from many sources. The quality information, in the design, can be easily accessed and implemented, unlike in the EDM.

Data Processes

Data and information are essential for the proper functioning of any healthcare system. Data is first collected, before being shared across the system (Burton. et al, 2017). Data collection systems include the healthcare surveys, enrolments, and records of billing, and any other records that are used by physicians. The data collected is shared with diverse relevant dockets within the healthcare system, for management. Management involves the dockets protecting and storing the data. Management is guided by principles that ensure that the patients are viewed holistically, their communication is improved and the treatments are personalized.

Conclusion

In conclusion, data collection processes and systems in the healthcare units are essential. The information received is important in the efficiency of billing processes and the transformation of care delivery. The warehousing designs and models determine the success in the accuracy of data collected and stored.

References

Blasch, E., Ravela, S., & Aved, A. (Eds.). (2018). Handbook of dynamic data-driven applications systems. Springer. https://books.google.com/books?hl=en&lr=&id=CKR5DwAAQBAJ&oi=fnd&pg=PR5&dq=applications/systems+for+clinical+classification+and+coding+&ots=6OkyiPopbN&sig=kxo_7b33Qvuy_vuLSdhN_gkzHq8

Burton, P. R., Banner, N., Elliot, M. J., Knoppers, B. M., & Banks, J. (2017). Policies and strategies to facilitate the secondary use of research data in the health sciences. https://academic.oup.com/ije/article-abstract/46/6/1729/4159461

Cardoso, L., Marins, F., Quintas, C., Portela, F., Santos, M., Abelha, A., & Machado, J. (2018). Interoperability in healthcare. In Health Care Delivery and Clinical Science: Concepts, Methodologies, Tools, and Applications (pp. 689-714). IGI Global. https://www.igi-global.com/chapter/interoperability-in-healthcare/192700

Knowles, R., Colson, D., & Dezateux, C. (2016). Life Study Ethics and Information Governance Framework. https://discovery.ucl.ac.uk/id/eprint/1485684/7/Life%20Study%20Ethics%20and%20Information%20Governance%20Framework%201485684.pdf