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Health Information Improvement Proposal

Health Information Improvement Proposal

Compliance with Meaningful Use Guidelines is key when using a certified electronic health record (EHR) system. Healthcare organizations, physicians, and other interdisciplinary team members should collect and interpret patient data to enable them to enforce patient-centered services that improve clinical outcomes. Accordingly, this paper summarizes findings in the organizational EHR, evaluates the relevance of data to the goals of the organization, describes the latest trends in data analytics, and recommends steps to be adopted by the organization.

Recommendations Related to Technological and Logistical Changes

The recommendations are based on the fact that the healthcare organization was not fully compliant with the Meaningful Use Guidelines at the time of evaluation. The non-compliance was evidenced by various findings. Firstly, the healthcare organization was not using a certified electronic health record system. Accordingly, the first recommendation is the acquisition of certified electronic health record technology. The certified electronic health record system is desirable because it facilitates systematic and structured data storage (, n.d.). By so doing, members of the interdisciplinary team can easily access and retrieve patient information (, n.d.). Subsequently, the transfer and sharing of permissible patient information are effective. As a result, this promotes an interdisciplinary approach in the clinical decision-making process, hence better treatment and intervention plans and improved patient outcomes (, n.d.).

The second recommendation includes the utility and adherence to interoperability standards. Interoperability allows different health information systems to access, transmit, share, and integrate healthcare information (Rathert et al., 2019). This can happen within the healthcare facility, among facilities in different geographical locations in the state, or in facilities in different countries. This ensures that permissible patient information is transmitted promptly and promotes interprofessional collaboration in the management of patients (Kharrazi et al., 2018).

According to HIMSS (n.d.), interoperability has four levels: foundations, structural, semantic, and organizational. Furthermore, interoperability standards aim to establish commonality that ensures efficiency in data sharing (Rathert et al., 2019). Examples of the standards include terminology, transport, privacy and security, identifier, and current standards (HIMSS, n.d.). Therefore, the healthcare organization should work with relevant stakeholders to ensure that these standards are fully incorporated into the current electronic health record system.

Moreover, the third recommendation is staff training. Currently, healthcare providers at the facility are not trained periodically on the appropriate use of EHR technology. This has contributed to the non-compliance with the guidelines of Meaningful Use. Healthcare providers should be trained regularly. The training should focus on the appropriate use of EHR software and hardware and the legal and regulatory aspects of using EHR (Fragidis & Chatzoglou, 2018). This training will entail educating healthcare providers on the Meaningful Use Guidelines, the standards of interoperability, and relevant Acts such as the Health Insurance Portability and Accountability Act (HIPAA) (Moore & Frye, 2019).

This training is relevant to help healthcare providers modify and improve their current undesirable practices, such as the failure to provide electronic health information to patients after their treatment services. Furthermore, understanding the Meaningful Use Guidelines enables them to recognize the implications of non-compliance (Rathert et al., 2019). These implications include penalties and withdrawal of monetary reimbursements (Rathert et al., 2019). Therefore, regular staff training will ensure that proposed certified EHR, interoperability standards, and Meaningful Use Guidelines are adhered to and the technological changes are effectively implemented (Fragidis & Chatzoglou, 2018).

The other recommendation entails routine maintenance and performing periodic performance evaluations. Routine maintenance will ensure that both the hardware and software are working optimally. This should be accomplished by specialists such as quality assurance engineers, software developers, and specialists in health informatics (Fragidis & Chatzoglou, 2018). They should ensure that information storage, retrieval, and transfer are efficient and reproducible. Consequently, this will ensure that the interdisciplinary team of healthcare providers can use the certified EHR system without any technical or technological challenges (Fragidis & Chatzoglou, 2018). Evaluation should be performed regularly and should target the quality of service delivery and desirable changes in the workflow. The evaluation should rely on data obtained from patients, healthcare providers, and other stakeholders such as the hospital management (Fragidis & Chatzoglou, 2018).

Data Products and Outcomes from Recommendations

The recommendations can help achieve various data products and outcomes that are harmonious with the administrative and clinical goals of the healthcare organization. The administrative goal of the organization is to achieve a competitive advantage by compliance with regulatory and legal requirements when discharging healthcare services. The organization’s clinical goal is to use a competent workforce and evidence-based practices to provide patient-centered services, improve patient outcomes, and achieve patient satisfaction.

The certified electronic health system facilitates the systematic and structured storage of patient information. As a result, the certified EHR technology allows ease of storage, retrieval, and transmission of permissible patient information under the HIPAA (Rathert et al., 2019). This is an enabler of an interdisciplinary approach to the management of patients. Nurses, physicians, pharmacists, and laboratory technicians can collaborate in managing a patient because they can access patient information. After that, each member can make specific interventions. Examples of such interventions include seeking clarifications, making consultations, making necessary modifications in a treatment plan or order, and communicating the latest practices in managing the specific condition (Fragidis & Chatzoglou, 2018).

Accordingly, an interdisciplinary approach to clinical decision-making is achieved. An interdisciplinary approach allows the use of current evidence-based practices to manage the patient, hence, better quality of service provision (Rathert et al., 2019). Quality services imply that the patient’s recovery rates will increase, hence better outcomes and better satisfaction with services provided by the healthcare facility (Fragidis & Chatzoglou, 2018). Therefore, by facilitating systematic and structured storage, the certified EHR technology fulfills the clinical goal of the organization.

According to HIMSS (n.d.), the adoption of interoperability helps to establish the terminology, transport, privacy and security, identifier, and content standards. The privacy and security standards reiterate the importance of HIPAA and identify protected healthcare information (HIMSS, n.d.). Adherence to this standard ensures that the healthcare organization fulfills its administrative goal of complying with regulatory and legal requirements.

According to HIMSS (n.d.), content standards, identifier standards, and transport standards establish the types of information that can be shared and how to identify patients and healthcare providers uniquely. These three standards facilitate interprofessional collaboration during patient management (HIMSS, n.d.). Furthermore, they enable an accurate record of data registries that can be used to make case reports. This provides healthcare providers with the latest clinical trends and enables them to uphold evidence-based practices when managing the patient (Rathert et al., 2019). The quality-of-service delivery will be enhanced through evidence-based practices, hence better patient outcomes. Furthermore, utilizing terminology standards promotes open communication and enables an interdisciplinary approach to decision-making (HIMSS, n.d.). Therefore, interoperability helps fulfill both the administrative and clinical goals of the organization.

Furthermore, staff training equips the interdisciplinary team members with the current knowledge and skills. Knowledge about the Meaningful Use Guidelines is important to achieve compliance and avert penalization (Rathert et al., 2019). By so doing, the administrative goal of the organization is met. The Meaningful Use Guidelines will enable the healthcare facility to address the current shortcomings, such as the lack of case study reports. Case study reports equip staff with relevant and current information about managing a specific group of patients. Accordingly, the quality of service delivery will increase, and better patient outcomes will be achieved (Rathert et al., 2019). Subsequently, this fulfills the organization’s clinical goals. Furthermore, training will enable staff to know the standards of interoperability. The utility of these standards, as aforementioned, helps to promote compliance with the legal and regulatory requirements and to improve patient outcomes.

Regular evaluation will provide data on patient satisfaction rates, quality of service delivery, healthcare provider satisfaction levels, and the satisfaction levels of the hospital administration. Better patient satisfaction rates reveal that the quality of healthcare services has been improved after implementing the recommendations (Fragidis & Chatzoglou, 2018). Additionally, it demonstrates compliance with the clinical goal of the organization. Better satisfaction levels of the hospital administration demonstrate achievement of both organizational and clinical goals. Lower satisfaction levels after performance evaluation demonstrate poor performance and will form the basis for making appropriate interventions to ensure the fulfillment of organizational and clinical goals.

Contemporary Data Analysis Trends

Current data analysis trends can be used to improve practices in the organization. The first data analysis trend is artificial intelligence (AI). AI refers to the ability of machines or robots to accomplish tasks that humans traditionally do because they require intelligence and significant discernment (Gill, 2022). In the context of healthcare organizations and EHR, artificial intelligence can use data registries to develop case reports and predict the trends of disease patterns in the hospital (Gill, 2022). Notably, this improves workflow because it eliminates the necessity of the interdisciplinary team to develop monthly case reports periodically. Healthcare providers can use this information to develop evidence-based practices. In addition, AI can be used to monitor tasks done by the interdisciplinary team and predict or establish the level of compliance with legal or regulatory requirements. This can be achieved by programming computers or machines with the current guidelines on meaningful use and interoperability (Gill, 2022). After that, the computers will evaluate all tasks, check for any breach of the guidelines, and give the percentage of compliance. Ultimately, this helps to boost operational efficiency and ensures periodic feedback to healthcare providers (Gill, 2022).

The second data analysis trend is the utility of AnalyticsOps. This refers to a framework that focuses on automating tasks (Gill, 2022). This is achieved using the information in the data registries (Gill, 2022). Therefore, risks associated with manual operations are minimized. Additionally, AnalyticsOps makes operations to be scalable (Gill, 2022). It helps to test, deploy, monitor, and adapt desirable analytics that can be used by the organization (Gill, 2022). In the context of EHR, this can be used to automate services such as billing and providing electronic health information to patients during discharge or after treatment. Furthermore, it can be used to create interdisciplinary collaboration in the management of patients because they are considered to be part of the analytics team.

The other data analysis trend that can be utilized is data democratization. This analysis method aims at ensuring that all stakeholders have access to information (Gill, 2022). It eliminates existing barriers to accessing information. Data democratization gives an organization a competitive advantage because it equips all members with current and relevant information (Gill, 2022). By so doing, interprofessional collaboration is achieved because people specialized in a specific field can identify critical issues and make proposals on mitigation strategies (Gill, 2022). In the context of the EHR, data democratization can be used to achieve interprofessional collaboration and ensure that patients access and transmit their electronic health information. However, caution should be exercised to ensure compliance with the HIPAA. Sharing of permissible patient information will ensure that the HIPAA is not breached.

The other strategy is the use of hybrid cloud computing. Hybrid cloud computing involves private and public cloud computing (Gill, 2022). Notably, it creates flexibility in the transfer of data. Furthermore, it helps to create backup and enforce the security of data (Gill, 2022). This can be applied in the context of EHR by establishing both private and public clouds. It is useful to prevent patient data loss and ensure that the data is safe. Furthermore, it creates flexibility in communication between healthcare providers or between hospitals or nations when the standards of interoperability are adopted.

Recommendations for Collecting Data, Securely Storing Data, and Converting Data Analytics

Different practices can be used in collecting, storing, and processing data. During data collection, the first practice to uphold is honesty and transparency (AHRQ, n.d.). Patients should be aware of the rationale for providing their details. They should know that the information is for treatment purposes and will be kept private and confidential (AHRQ, n.d.). The second practice during data collection is knowing the regulatory or legal provisions of the state or nation (AHRQ, n.d.). An example is being aware of the protected patient information as stipulated by the HIPAA (AHRQ, n.d.). This helps avoid sharing or transmitting patient information that is not permissible, hence the risk of penalization (AHRQ, n.d.). The other practice to adopt during data collection is upholding healthcare ethics. Examples include patient autonomy, justice, beneficence, and nonmaleficence. The collected data should aim to promote the well-being of the patient and not inflict any harm.

Different strategies should be adopted to ensure that the data is securely stored. An example is data encryption (AHRQ, n.d.). Data encryption is important because it is encoded. Encryption is desirable even when a person manages to get past the other security options of the system (AHRQ, n.d.). The other methods to keep information secure include the use of passcodes, backing up data, and the utility of firewalls and antivirus protection (AHRQ, n.d.).

Different practices can be used to convert data analytics into useful and understandable deliverables. The first strategy involves experts: data analytics specialists, chief analytics officers, and data managers (Kharrazi et al., 2018). Analytics specialists help analyze data, establish patterns and trends, and make evidence-based recommendations (Kharrazi et al., 2018). In the end, this ensures that the most appropriate and understandable deliverables are formulated. Chief analytic offers are actively involved in interpreting and making decisions based on the analysis findings (Kharrazi et al., 2018). Data managers uphold the integrity of information in the data registries or the EHR system. Data governance managers ensure the integrity of information (Kharrazi et al., 2018). Therefore, healthcare providers and the hospital administration should work in concert with data analysis specialists to ensure that useful deliverables are formulated.

Evidence and Best Practices to Target Proposal Messaging to Stakeholders

Different strategies can be incorporated to create stakeholder buy-in and foster the implementation of the recommendations. Stakeholder mapping is a strategy that involves identifying key stakeholders and the unique roles they play in the implementation process (Hickey et al., 2018). It makes the stakeholders understand their importance and relevance, increasing the likelihood of buy-in (Hickey et al., 2018). The other strategy is the evaluation of stakeholders’ expectations or needs (Alexander, 2018). The unique needs of the key stakeholders should be addressed to increase the likelihood of creating buy-in from the stakeholders (Hickey et al., 2018). The third is communicating the goals and objectives of the change (Alexander, 2018). This is important because stakeholders understand the value and merit that accompany the change. The fourth technique strategy is upholding honesty (Alexander, 2018). The potential shortcomings of the change should be identified and explained. Honest cultivates trust and increases the possibility of stakeholder buy-in (Hickey et al., 2018).


The recommendations are based on the fact that the healthcare organization was not fully compliant with the Meaningful Use Guidelines at the time of evaluation. The data products and outcomes align with the administrative and clinical goals of the organization. Data analysis trends such as artificial intelligence can be adopted to improve the organization’s current performance (Gill, 2022). Strategies like data encryption can be used to uphold the security of data during collection and storage (AHRQ, n.d.).


AHQR. (n.d.). Improving Data Collection across the Health Care System.

Alexander, M. (2018). 6 Ways to Increase Buy-In From Project Stakeholders. (n.d.). Certified EHR Technology.

Fragidis, L. L., & Chatzoglou, P. D. (2018). Implementation of a Nationwide Electronic Health Record (EHR): The International Experience in 13 Countries. International Journal of Health Care Quality Assurance, 31(2), 116–130.

Gill, S. N. (2022). 10 Latest Trends in Big Data Analytics for 2022 | Ultimate Guide.

Hickey, G., McGilloway, S., O’Brien, M., Leckey, Y., Devlin, M., & Donnelly, M. (2018). Strengthening Stakeholder Buy-In and Engagement for Successful Exploration and Installation: A Case Study of the Development of an Area-Wide, Evidence-Based Prevention and Early Intervention Strategy. Children and Youth Services Review, 91(June), 185–195.

HIMSS. (n.d.). Interoperability in Healthcare.

Kharrazi, H., Gonzalez, C. P., Lowe, K. B., Huerta, T. R., & Ford, E. W. (2018). Forecasting the Maturation of Electronic Health Record Functions Among US Hospitals: Retrospective Analysis and Predictive Model. Journal of Medical Internet Research, 20(8), 1–11.

Moore, W., & Frye, S. (2019). Review of HIPAA, Part 1: History


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Health Information Improvement Proposal

Health Information Improvement Proposal

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