Benchmarks and Quality Measures

Benchmarks and Quality Measures

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Identify the benchmarks and quality measures used to compare with the office data for your proposal (Excel spreadsheet). Assess the compatibility of the proposed data and examine potential issues related to information quality (3 pages).

A key part of your proposal will be to identify benchmarks and trends for the topic you have chosen for your documentation review. Benchmarks can come from national or state quality standards or trends. If your proposal is approved, you as the office manager will want to try to answer this question: How does our office data compare to national or state trends?

You need to identify your benchmarks before you can collect and then compare the data. You decide what your benchmarks are. They could be based on national averages, state averages, or quality standards. For example, here is one quality standard: All patients with chronic, stable coronary artery disease are on an antiplatelet therapy or have supporting documentation as to why they cannot take an antiplatelet therapy. For instance, they may have an allergy.

Another question to consider when establishing benchmarks is this: Are you comparing apples to apples or apples to oranges? In addition, if you are retrieving information from a national database or data from an HIE, how do you know your office data is comparable to the information you are retrieving? Consult your suggested resources for answers to these questions.

For this second assessment, you are to:

Analyze statistical trends and assess quality measures relevant to your proposal.
Assess the compatibility of data drawn from multiple sources.
Determine the effects of health information quality on an HIE.

This assessment is completed in three steps:

Step One – Preparation: Locate data related to quality measures or trends relevant to your topic from specific websites.
Step Two – Data Collection: Create a data collection tracking spreadsheet and dashboard.
Step Three – Data Compatibility: Write a short paper on data compatibility and quality.

Please study this assessment’s scoring guide to better understand the performance levels relating to each criterion on which you will be evaluated.
Step One: Preparation

Locate data related to quality measures relevant to your topic from one or more of these websites:

Agency for Healthcare Research and Quality. (n.d.).
Centers for Disease Control and Prevention. (n.d.).
The Joint Commission. (n.d.).
NCQA. (n.d.).
Occupational Safety and Health Administration. (n.d.).
Any other site that contains national or state health care quality measures.

Step Two: Data Collection

Using the Data Collection Spreadsheet Guide [XLSX] as an example, create a spreadsheet containing three tabs: Dashboard Tracking, Data Collection, and Trending.

On the first tab, Dashboard Tracking, draw from the information you gathered in Step One as part of your preparation for this assessment:

Identify the specific benchmark data you will compare with your office data. Remember it is up to you to establish your benchmarks.
Organize or create a spreadsheet to display the totals, percentages, averages, and so on of your office data and of the national or state data you will be using for comparison. Note: Your Office Data column will be blank because you are not collecting any office data. This is only a proposal to do an information review of the quality of care provided by the physician group. Data does, however, need to appear in the Benchmark (national/state) data column.
Include at least one comparison graph of your choice on this tab.

On the second tab, Data Collection, draw from the information you gathered in Step One as part of your preparation for this assessment:

Create a form you will use to collect specific data from the patients’ records.
Include a row for each patient.
Provide a column for each data collection point (quality measure) you will be comparing.

Note: The information on this page is totaled, averaged, et cetera, with the results linked to the first tab.

To create your third tab, Trends, you will need to do some additional research. Identify national benchmarks for the condition you have chosen that could be compared to your office data. For example, if the trend in your office is that you are seeing more patients with asthma, but the national trend is decreasing, you have discovered a discrepancy that needs to be investigated.

To perform your analysis:

Visit one or more of the following websites containing national data:
Agency for Healthcare Research and Quality. (n.d.). Healthcare cost and utilization project.
Centers for Disease Control and Prevention. (n.d.). CDC WONDER.
Any other site that contains national or state health care data.
Locate and analyze statistical data relevant to the selected condition.
Examine trends:
What other meaningful trends exist? For example, consider the number of new cases, increases or decreases of cases within a specific age range or location, et cetera.
How do the national and state trends compare?
Is the national trend increasing or decreasing?
What is the percentage of cases who expire from the disease?
Identify the trending of one statistical result relating to the condition you selected over the last 5–10 years.
Create a line graph on the third tab of your spreadsheet, Trends, that illustrates the national and/or state trending of the disease you selected over the past 5–10 years.

Note: Remember you have not collected your office data yet for comparison purposes. You could add that data at a later time.
Step 3: Data Compatibility

Write a short section to add to the proposal you will complete in Assessment 3. Be sure this section of your proposal includes all of the following headings and your narrative addresses each of the bullet points.

Provide a brief 1–2-sentence high-level summary explaining data compatibility.

Data Compatibility

Assess the compatibility of the data:
How can you ensure data from multiple sources is compatible?
How do you know the data you are using for comparison is compatible with your office data?
What challenges are associated with data standardization? We do not want to compare apples with oranges. You want to be sure data from multiple sources:
Represents the same condition.
Uses similar statistical analysis.
And so on.

Effects of Health Information Quality on the HIE

Explain the difference between an HIE and a national database.
Explain what problems can develop if facilities submit incomplete or inaccurate information to an HIE.
Explain what problems can develop if facilities submit incomplete or inaccurate information to a national database.
Explain how incomplete or inaccurate data may affect your proposal.


Briefly reinforce your paper’s main points.

Additional Requirements

Your assessment should meet the following requirements:

Excel spreadsheet: Your spreadsheet must contain three tabs, be organized, contain appropriate graphs, and have correct spelling.
Written communication: Your paper does not need to be in APA format. It does need to be clear and well organized, with correct spelling, grammar, and syntax, to support orderly exposition of content.
Title page: Develop a descriptive title of approximately 5–15 words. It should stir interest yet maintain professional decorum.
References: Include a minimum of two citations of peer-reviewed sources in current APA format.
Length: 1–3 typed, double-spaced content pages, not including the title page and references page.
Font and font size: Times New Roman, 12 point.

Competencies Measured

By successfully completing this assessment, you will demonstrate your proficiency in the following course competencies and scoring guide criteria:

Competency 4: Determine how a health information exchange (HIE) or other external health care databases affect the management of patient data, clinical knowledge, and population data.
Assess the compatibility of data from multiple sources.
Explain the effects of health information quality on an HIE.
Competency 5: Apply quality and change management concepts to health care information management.
Analyze statistical trends relevant to a selected condition.
Assess quality measures relevant to a selected condition.
Competency 6: Communicate in a manner that is scholarly, professional, respectful of the diversity, dignity, and integrity of others, and is consistent with the expectations for healthcare professionals.
Write clearly, with correct spelling, grammar, and syntax, and good organization.
Apply proper APA formatting and style to citations and references.

Data Collection

As you review these resources, please keep these questions in mind:

What is the difference between a national benchmark and a quality measure?
How are benchmarks used in healthcare information?

Data Collection

Review the following:

Oachs, P. K., & Watters, A. L. (2020). Health information management: Concepts, principles, and practice (6th ed.). AHIMA Press. Available in the courseroom via the VitalSource Bookshelf link.
Chapter 13, “Health Information Systems Strategic Planning.”
Bata, S. A., & Richardson, T. (2018). Value of investment as a key driver for prioritization and implementation of healthcare software. Perspectives in Health Information Management, Winter 2018, 1–13.
Electronic medical record systems represent a major investment for healthcare systems. Return on Investment (ROI) is often calculated to justify the selection of an IT project; but, this approach has limitations. This scholarly article explores a different approach—the Value of Investment process—to explore the efficacy of this methodology.
Health Care Administration Undergraduate Library Research Guide.

Data Compatibility

As you review these suggested resources, please consider these questions:

What are data standards?
How can you ensure data compatibility?

Data Compatibility

Review the following:

Oachs, P. K., & Watters, A. L. (2020). Health information management: Concepts, principles, and practice (6th ed.). AHIMA Press. Available in the courseroom via the VitalSource Bookshelf link.
Chapter 5, “Clinical Classifications, Vocabularies, Terminologies, and Standards.”
Chapter 14, “Consumer Health Informatics.”
Davidson, J. E. (2017). Organizing the evidence for healthcare design projects. Health Environments Research & Design Journal, 10(2), 13‒22.
“This methodology column provides project leaders with helpful tools to organize the evidence analysis for healthcare design projects” (p. 13).
Galetsi, P., Katsaliaki, K., & Kumar, S. (2019). Values, challenges and future directions of big data analytics in healthcare: A systematic review. Social Science & Medicine, 241, 112533.
The use of analytical techniques in healthcare offers the ability to personalize health services for individual users and challenge the power issues in the traditional doctor-patient relationship. This article offers a literature review of big data analytics in health to identify the organizational and social values along with associated challenges.
Gjellebæk, C., Svensson, A., Bjørkquist, C., Fladeby, N., & Grundén, K. (2020). Management challenges for future digitalization of healthcare services. Futures, 124, 102636.
eHealth offers a potential solution to various challenges in healthcare, but its use is underdeveloped. This article explores strategies that can facilitate workplace learning when introducing eHealth and new ways to provide healthcare services.
Keller, M. E., Kelling, S. E., Cornelius, D. C., Oni, H. A., & Bright, D. R. (2015). Enhancing practice efficiency and patient care by sharing electronic health records. Perspectives in Health Information Management, 1‒8.
“This study presents one example of a health information technology-based solution involving shared access to an electronic health record (EHR), and describes a case in which a physician’s office and a community pharmacy experimented with this model to promote practice efficiency while also providing enhanced access to clinical information in both directions” (p. 1).

Please consider these questions as you review the suggested resources for this assessment:

How does documentation affect the quality of patient care?
What is the difference between an HIE and a national database?

Effects of Information Quality on the HIE

Review the following:

Oachs, P. K., & Watters, A. L. (2020). Health information management: Concepts, principles, and practice (6th ed.). AHIMA Press. Available in the courseroom via the VitalSource Bookshelf link.
Chapter 15, “Health Information Exchange.”
Khurshid, A., Diana, M. L., & Jain, R. (2015). Health information exchange readiness for demonstrating return on investment and quality of care. Perspectives in Health Information Management, 1‒15.
“The shift from fee-for-service to fee-for-outcomes and fee-for-value payment models calls for care providers to work in new ways. It also changes how physicians are compensated and reimbursed. These changes necessitate that healthcare systems further invest in information technology solutions” (p. 1).
Information Collection
Cancer is the greatest cause of death worldwide. According to the World Health Organization (2022), it was responsible for roughly 20 million deaths in 2020 for six months period. If discovered early and correct medical care is provided to patients, the condition can be treated. This study will look into how good data collecting and storage can enhance outcomes for cancer patients. According to Mazor et al. (2020), a full and detailed collection of data for every cancer case is critical in understanding the condition and how it might be properly treated. Molecular abnormalities are bound to evolve over time, complicating treatment. Large data sets are required for research and, as a result, treatment of rare cases of cancer. Cancer was chosen for this case study for these reasons.
Information gathering
Data collection from cancer patients in low-income areas and from minority groups is critical for improving health outcomes. Although cancer affects all communities, patients with poor incomes and from minority groups such as African Americans, Hispanics, and indigenous peoples may be denied treatment and early detection (Mazor et al., 2020). In this aspect, health inequities and negative outcomes predominate. Patient data collection, storage, and analysis will lead to patient-centered and evidence-based solutions for marginalized groups.
Data from cancer patients must be retrieved via electronic medical records. Information systems that replace paper patient records are efficient, accurate, and simple to obtain. Cloud storage can be utilized to store sensitive data, hence increasing data security. The case study will evaluate electronic medical record information on minority groups and patients from low-income locations. The data in the records is likely to be relevant, accurate, and well-organized.
Notably, the information gathered should be broad in order to provide an accurate assessment. It is critical to determine the frequency of cancer among underserved communities, effective cancer treatments, and whether or not health disparities exist in the community of study. As a result, electronic health records from doctors’ offices, hospital admissions, and emergency room visits should be discovered and evaluated. Patients’ racial backgrounds and insurance details should be given special consideration; cancer patients from minority groups and uninsured cancer patients will be identified and reviewed. To determine the health outcomes and inequities for marginalized communities in the United States, the assessment must contain cancer patients’ information for ten years.
To acquire a thorough understanding of cancer outcomes in marginalized populations, it is critical to study many sources of documentation. As a result, the information evaluated will comprise the patient’s history, physician progress notes, laboratory results, radiological data, and discharge narrative. Because it is complete and accurate, the information will be retrieved by computerized physician order entry. It must also come from point of care, pharmacy laboratories, and management system outcomes. Furthermore, administrative and clinical procedures will be employed to obtain permission for data gathering.
Life Cycle of Information
The first step in collecting data from cancer patients is to learn about the administrative processes and procedures. I will personally write a formal letter to the administration requesting guidance on the procedures necessary to acquire pertinent data. Data will be taken from various electronic medical records and stored on a computer after permission is obtained. Codes must be used to protect sensitive patient data, such as their names and ethnicity. The computer must also be secured using software packages to prevent unauthorized access to the stored data. To limit computer use to two people, strong passwords will be utilized.
Notably, interoperability standards will be harmonized and implemented into the data management system in order to suit the case’s healthcare needs for information sharing. According to Aochs and Waters (2020), three components must be present: shared content, shared information exchange infrastructure, and shared information system rules. The shared content in this example is reliable information on cancer patients from underprivileged populations in the United States. The technology utilized to share data is cloud-based; it is simple to use while remaining secure. The guidelines for information exchange will include restricting the amount of people who have passwords, using codes to characterize patient data, and encrypting data.
Integrating office data with health information exchange is advantageous since it improves interoperability. Healthcare providers can identify strategies to assist vulnerable populations with value-based care. Because healthcare professionals may access and communicate crucial data information, the method also ensures continuity of service (Abouelmehdi et al., 2018). However, the integration is susceptible to limited patient data privacy and confidentiality. If data breaches occur during data exchange operations, legal action is taken. Certain standards and regulations must be followed by healthcare professionals to ensure that patient information is fully secured. Because staff must be trained, the procedure can be costly and time-consuming.
Standardizing health information is difficult since existing information systems may lack these capabilities. The standards are also not being implemented adequately due to a lack of training, and the required guidelines have not been properly defined (Aochs & Waters, 2020). Creating unique patient identification is difficult in many contexts. Enforcing standards in healthcare settings is extremely difficult due to the uniqueness of each facility. Coordination of efforts among parties is also difficult. The issues are related with patient hazards, inefficient use of healthcare resources, and limited medical knowledge advancement.
The physical destruction of hard drives will be used to destroy patient data. Other parties must be unable to use the material (Abouelmehdi et al., 2018). Furthermore, material stored in the cloud should be permanently destroyed. Experts in the sector should be hired to ensure that the destruction procedure is both successful and economical. After the analysis is completed, the data will be discarded.
Considerations Legal
Privacy, confidentiality, and data security are critical during the collecting and processing of information. Data privacy refers to the controlled access to information of marginalized cancer patients. The revelation of data from cancer patients is referred to as confidentiality (Aochs & Waters, 2020). Even if the healthcare provider has no relationship with the population in question, they are required to secure information (Sorbie, 2020). When information is safeguarded from both purposeful and unintentional leakage, data security triumphs. Hardware, software, and staff in healthcare institutions may be used as protective protections.
Various regulations are in place to protect the confidentiality, security, and privacy of cancer patients from underserved communities. The first is the Health Insurance Portability and Accountability Act, which prohibits people from releasing sensitive medical information (Abouelmehdi et al., 2018). Patients are additionally safeguarded by the Uniform Care Information Act, Medicare and Medicaid laws, and the California Confidentiality of Medical Information Act (if any of the patients live in California) (Aochs & Waters, 2020). The Federal Fair Credit Reporting Act governs data collection and use by encouraging customers to report a data breach in writing or orally. Understanding the rules and applying them in the collection and use of patient data is required for the creation and implementation of non-disruptive solutions.
The case study’s information requires patient health information. It will be used to discover demographic concerns that contribute to the occurrence of cancer in specific communities as well as treatment difficulties. It will also be used to develop individualized therapy choices for those impacted. The Health Insurance Portability and Accountability Act will have an impact on how patient health information is collected and shared (Aochs & Waters, 2020). Secure software and hardware must be used to share data. To avoid exposure to third parties, strong passwords must also be utilized. Individuals who violate the act shall be held responsible and punished in a court of law. They should also be disciplined or penalized at the level of the healthcare facility. The statute will, in fact, be integrated into the code of conduct that would govern data gathering methods.
To summarize, it is critical to gather, analyze, and use data from a healthcare organization on the prevalence of cancer among minority groups and patients from low-income locations. It sets the path for suitable policies to be implemented to eliminate health disparities in communities. Evidence-based remedies, in particular, can be used to address the negative impact of cancer on underprivileged groups. Relevant data should be acquired from hospitals with the permission of administrators. It should also be safeguarded to ensure compliance with rules and regulations such as the Health Insurance Portability and Accountability Act.
K. Abouelmehdi, A. Beni-Hessane, and H. Khaloufi (2018). Security and privacy in big healthcare data. 1-18 in Journal of Big Data, 5(1).
P. K. Aochs and A. L. Waters (2020). Concepts, principles, and practice of health information management AHMA Press.
Mazor, M. B., Li, L., Monilo, J., Allen, O. S., Wisnivesky, J. P. & Smith, C. B. (2020). Disparities in supportive care needs over time between racial and ethnic minority and non-minority patients with advanced lung cancer. Journal of Pain and Symptom Management, 63(4), 563-571.
Sorbie, A. (2020). Sharing confidential health data for research purposes in the UK: Where are publics in the public interest? Evidence & Policy, 16920, 249-265.
The World Health Organization, (2022). Cancer.

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