Info.docx - Howard Assignments

Health Information Technology (HIT)

Influence on Clinical Outcomes

Influence on Organizational Processes Improvement

Challenges (administrative and managerial)

Telehealth

· Makes it possible to get medical services remotely thus improving healthcare access.

· Allows online consultations with healthcare professionals.

· Remote monitoring (Gajarawala & Pelkowski, 2021).

· Reduced wait times.

· Easier access to medical professionals.

· Increased convenience.

· Issues of privacy and security of personal health data electronically transmitted.

· It is impossible to do each type of visit remotely.

· Reduced finances due to lack of funding for some services causing out-of-pocket costs (Gajarawala & Pelkowski, 2021).

Electronic Prescribing

· Possible to monitor medical non-adherence.

· Enhanced medication safety via access to patient medication history in real-time.

· Prevention of medication errors and increased accuracy of medical prescriptions.

· Cost savings.

· Enhanced efficiency of the prescription process such as filling, routing, and distribution.

· Improved pharmacy workflow.

· Coordination and streamlining of medication management.

· Reduced legal risks and penalties due to adherence to prescription regulations.

· Possible prescription errors.

· Possible poor design of the software used for e-prescription.

· Workflow disruptions.

· Need for the training of staff and adoption by users.

· Issues with standardizing data about medications.

· Technical challenges of implementing and integrating the new systems into the existing systems (Wrzosek et al., 2020).

Electronic Health Records (EHRs)

· Improve access to patient information

· Mitigates medical errors.

· Aids in evidence-based decision-making.

· Ease administrative procedures.

· Improves coordination or care.

· Enhances communication among healthcare providers.

· Issues and concerns of security and privacy.

· High initial costs of implementation as well as maintenance expenses.

· Staff resistance.

· Extra time and effort of training.

· Limitation of technical resources.

· Workflow break up (Alzu'bi et al., 2021).

Health Information Exchange (HIE)

· Aids data sharing of patient information.

· Boosts coordination of care.

· Allows informed decision-making.

· Increases accessibility of data by medical professionals.

· Mitigates redundancy and duplication of procedures and tests.

· Enhances transitions in care.

· Privacy and confidentiality issues.

· Issues of data ownership and governance.

· Cost of HIE.

· Complexities in matching patients to their health records.

· Challenges with authorization or patient consent (Nahm ET AL., 2020).

Clinical Decision Support Systems (CDSS)

· Potential to mitigate care errors and thus improve the accuracy of diagnosis and treatment.

· Enhance adherence to evidence-based medicine.

· Seamless connectivity between the user and the system.

· Generates actionable insights through informed decision-making.

· Offers timely and accurate information to medical providers about a patient’s care.

· Aids adherence to clinical guidelines.

· Diagnostic errors and missed information depend on the quality of data.

· Digital health literacy.

· Deficiency of interoperability and transportability.

· Disruption of workflow.

· Alert fatigue and unwarranted alerts (Misro et al., 2023).

Healthcare Management Information Systems (HMIS)

This system is utilized for the collection, storage, analysis, and evaluation of health-related data from a healthcare setting to local, regional, and national levels of administration (Endriyas et al., 2019). It offers analytical reports and visuals that support decision-making at all the stated levels.

Data Acquisition

This process involves the production and gathering of timely, accurate, and relevant data. Generation of data is accomplished via the entry of standard coded formats hence enabling quick mechanical data capturing and reading. Data gathering is different from data generation in that data can be input directly at the source.

Data verification

This function entails authenticating and validating the collected data. This step utilizes the GIGO principle where data that contains inconsistencies and inaccuracies is spotted early enough in the system to enable immediate correction and minimization of the final costs of system output errors.

Data management

This functionality is inclusive of data storage, data classification, data computation, and data update. Data storage involves the preservation and archiving of data. Classifying data involves creating a taxonomy of the data that has been gathered and stored to increase the comprehension of how that data can be reused. Data updates are used to manage new and changing information and thus seek constant monitoring. Data computation entails manipulating and transforming data such as using mathematical models to enable further data analysis, understanding, and assessment.

Data retrieval

This functionality is concerned with the transfer and distribution of data. The process of data transfer is dependent on the time it takes for the required data to be transmitted from the source to the desired end user. However, a major barrier to this proves is the existence of noise or distortion that can occur internally or externally to the HMIS. The process of distributing data makes sure that data can be accessed at the time and at the place required. This step requires methods of authorization to ensure that the unintended users cannot access the sensitive data in the system. This can be accomplished via the integration of data security strategies such as passwords, access control, firewalls, etc.

Data presentation

This process entails how system users interpret the system-generated information. Summary tables and statistical reports can be generated when only tactical and operational managerial decisions are anticipated. However, some decision-making processes require critical thinking and actively collaborating with relevant stakeholders. Graphics can also be integrated for better managerial decision analysis due to the appearance offering a better intuitive feel of the patterns or trends of data (Endriyas et al., 2019).

Functionalities

· HMIS is used to manage healthcare professionals which is inclusive of scheduling employees, processing payrolls, and evaluating performance.

· It is also used to manage processes of billing, claims management, and management of finances this offering precise and accurate financial data.

· This system enables efficient registration of patients and appointment scheduling, making sure that all administrative processes are streamlined.

· HMIS helps in the management of inventories, maximizing levels of stock, and smoothening supply chain activities.

· HMIS also aids in reporting by producing reports and aiding the data analysis process to assist the decision-making process, tracking KPIs, and ensuring operational efficiency (Endriyas et al., 2019).

Main Users and Specific Needs

· Healthcare managers and administrators to handles processes of administration, effectively allocate resources, assess performance, and for decision-making.

· Billing specialists for coding, billing, and claims management.

· Financial experts for precise billing, claims management, and generating financial reports.

· HR department for handling staff information, creating payrolls, scheduling, and evaluating the performance of employees.

Value and Operability

HMIS adds value via the automaton of procedures, boosting productivity, and increasing financial management. It also smoothens processes, promotes data-driven decision-making, and ensures adherence to legal procedures.

Health Information Management Systems (HIMS)

The administration and organization of patient information are the key roles of HIMS. This system focuses on storing, retrieving, and exchanging electronic health records, and other health-related data (Stanfill & Marc, 2019).

Functionalities

· HIMS allows the creation, storage, access, and sharing of patient medical records in an electronic format boosting access to detailed patient data.

· This system supports documentation and coding of clinical information ensuring detail and consistency of patient data recordings. This may include treatment plans and diagnostic codes.

· HIMS can be used to support the decision-making process in healthcare settings via evidence-based practices and clinical standards made possible by features such as notifications for drug side effects and possible interactions.

· This system also allows safe information sharing about the health of patients between multiple healthcare settings, and systems thus promoting coordination and continuity of care.

· HIMS ensures compliance to set regulatory standards and quality reporting. It also monitors patient outcomes and performance measures (Stanfill & Marc, 2019).

Main Users and Specific Needs

· Medical care providers utilize HIMS for accessibility and updating of patient information enabling informed clinical judgments.

· Medical coders leverage HIMS for assigning diagnosis and medical procedure codes allowing appropriate billing and reimbursement.

· Technical experts use the system to ensure the security and privacy of patient information.

· HIMS is used by quality improvement experts to analyze data and generate quality reports making it easy to spot any issues and monitor patient outcomes.

Value and Operability

The utilization of HIMS enhances clinical judgment, and patient care coordination, and ensures regulatory compliance. It also ensures better patient outcomes by improving access to patient information, promoting evidence-based practices, and enabling the smooth transfer and sharing of information among medical providers.

References

Gajarawala, S. N., & Pelkowski, J. N. (2021). Telehealth benefits and barriers. The Journal for Nurse Practitioners, 17(2), 218-221.

Alzu'bi, A. A., Watzlaf, V. J., & Sheridan, P. (2021). Electronic health record (EHR) abstraction. Perspectives in Health Information Management, 18(Spring).

Misro, A., Mehta, A., Whittington, P., Dogan, H., Mishra, N., Kadoglou, N., & Theivacumar, S. (2023). From Concept to Reality: Examining India’s Clinical Decision Support System (CDSS) Challenges & Opportunities. medRxiv, 2023-04.

Endriyas, M., Alano, A., Mekonnen, E., Ayele, S., Kelaye, T., Shiferaw, M., … & Hailu, S. (2019). Understanding performance data: health management information system data accuracy in Southern Nations Nationalities and People’s Region, Ethiopia. BMC health services research, 19(1), 1-6.

Nahm, E. S., Schoenbaum, A., Behm, C., & Rowen, L. (2020). Health Information Exchange: Practical Overview and Implications for Nursing Practice. JONA: The Journal of Nursing Administration, 50(11), 584-589.

Stanfill, M. H., & Marc, D. T. (2019). Health information management: implications of artificial intelligence on healthcare data and information management. Yearbook of medical informatics, 28(01), 056-064.

Wrzosek, N., Zimmermann, A., & Balwicki, Ł. (2020, December). Doctors’ perceptions of e-prescribing upon its mandatory adoption in Poland, using the unified theory of acceptance and use of technology method. In Healthcare (Vol. 8, No. 4, p. 563). MDPI.