DOMAIN 3 DATA ANALYTICS AND USE
Explain the function(s) of a data dictionary
A data dictionary can be defined as a tool used by healthcare organizations for the purpose of ensuring accurate data collection. In order for data to be reliable and usable, all users and owners of the data must understand/interpret their meaning based upon the same source of truth- a data dictionary. According to a "Practice Brief" issued forth by the American Health Information Management Association, "standardizing data enhances interoperability across systems", and data dictionaries promote this necessary standardization. A data dictionary should describe the meaning of each data element. For example, (1) naming conventions must match between systems, (2) definition of data elements must be explained, (3) field lengths of data elements should match between systems, (4) data types ( alpha, numeric, etc) should match between systems, and (5) data frequency (monthly, annually, etc) should match between systems. To reiterate, the primary goal of a data dictionary is to achieve standardization of data elements between various systems.
Briefly explain how to build a data dictionary in Microsoft Access
A data dictionary will be compromised of field names, field types, tables, and reports. Microsoft Access is an excellent database tool to use in the healthcare setting. To create a data dictionary, there will need to be a database containing information needed to build the dictionary. The first step would be to open the appropriate database in Access, followed by clicking the "database tools" tab and the "database documenter." Next, select the table(s) to include in the dictionary and the queries to include. The "options" button will allow for selection of the various attributes needed to meet the unique characteristics of the desired data dictionary. If desired, a report can be run after these choices are made and the dictionary is built, and the report can be saved to an excel file through the "export" dialog box. Of note, the steps may vary depending upon the version of Microsoft Access being used. Data dictionaries may be built in other platforms, such as SQL, or Oracle.
Briefly explain the meaning and purpose of disease and immunization registries
A disease and immunization registry is a tool that tracks the clinical outcomes and associated treatments of a designated patient population. These registries typically house data pertaining to chronic diseases such as diabetes, heart disease, and lung disease. Through the collection of data in these registries, the quality of patient care can be improved because ultimately the data can portray opportunities for enhanced healthcare and improv patient outcomes. Health information management professionals should consider registry compatibility when selecting an electronic health record (EHR) platform and upgrading to a new EHR. It is advised to purchase and implement an EHR system that allows the healthcare entity the opportunity to create internal registries unique to their healthcare landscape. A beneficial registry will offer healthcare entities to produce disease-specific reports, progress reports, exception reports, and patient population reports.
Briefly summarize the components of a healthcare compliance audit report
A healthcare compliance audit report is the end product of an investigation into designated healthcare practices and its associated compliance risks. The structure of a healthcare compliance audit report should include the following components: background, scope, methodology, regulatory guidance, objectives, audit results, recommendations, an auditee response. The background covers the reason for the audit, such as identified errors, identified workflow process issues, regulatory focus etc. The scope explains the population, the sample size, references, and time frame. The methodology addresses the steps of the investigation (interviews, resources reviewed, data analytics, etc). Regulatory guidance provides the foundation for the audit's objectives (Medicare's national coverage determinations/local coverage determinations, Office of Inspector General Annual Audit plan, etc) Audit results focus on noncompliant issues described as error percentages and workflow inefficiencies, and recommendations are provided to help guide auditees in the development of their response.
Identify methods to ensure that audit reports are effective in their purpose
An audit report is created to be read, followed by actions being implemented. An audit report is the communication tool that, with excellent writing skills, can be the catalyst to effectively enforce compliance. An effective audit report, however, should not begin at the end of an audit. Communication between the auditor and the auditee(s) should start with preaudit meetings, through which the audit process and scope are defined, and the auditee is given the opportunity to express their thoughts and concerns. Open communication should be held throughout the audit fieldwork, factoring pertinent information attained into the audit report. a draft audit report should be provided to the audited manager(s) prior to a closing face-to-face meeting, allowing the manager(s) time to prepare a response. The use of audit report templates can also help the auditor write an effective message. Templates promote timeliness and conciseness in report writing.
Discuss best practices pertaining to the presentation of data
Data alone do not necessarily tell a story or show trends or even suggest success or failures. Compiling data and the analyzing them is one step in the right direction toward providing insight into successes and/or failures and subsequently assist in making decisions. After the data have been synthesized, the next step is to present the information, usually to management or administration. Excel is an excellent tool that can extract significance from big data, specifically through PivotTable functionality. Charts and graphs can subsequently be generated from PivotTable functions and then linked into a PowerPoint presentations or SharePoint dashboards. When creating visual presentations, determine the main objective that needs to be represented to the viewer. There should always be a singular point to make about each slide so that the viewer's comprehension or insight into the data's meaning will be quickly attained. Techniques to effectively communicate the meaning of data include the following: 1. Simplify the focus. 2. Be methodical and clear with the data presentation. 3. Eliminate distractions. 4. Choose the type of chart/graph that is impactful to the viewer (bar, pie, line graphs etc). 5. Replace text with visuals.
Identify ways to resolve anomalies of data findings
Data anomalies and/or data errors are all too common in healthcare documentation practices. Data integrity is critical to the promotion of patient safety, quality of care, and efficiency. To ensure data integrity and eliminate risk associated with data anomalies, it is imperative for a healthcare organization to prove that data are authentic, timely, accurate, and complete. Documentation policies and procedures should be created and implemented to comply with regulatory and accreditation guidelines. The policies should be enforced and monitored by governance controls. Templates and scripts can be designed to discourage copying and pasting notes by limiting field size and limiting text fields. Data models can be used to promote data integrity because they set parameters for the behavior of data and the communication between electronic health record systems. Standardization of data definitions and the structure of data fields will also limit the use of free text. Naming conventions, abbreviations, and acronyms should be standardized as another means of resolving data errors.
Discuss the concept of data dashboards
Data dashboards are becoming more frequently created and implemented in the healthcare environment. Dashboards are a snapshot reflection of analyzed data in various formats. Dashboards provide a quick glance (usually to the administration) of key performance indicators (KPIs) relevant to various processes (coding accuracy, case mix index trending, admission/discharge stats, etc) The dashboard is usually available on a secured webpage linked to a database (Access or Excel), and the supporting database is typically supplied with data by multiple departments who contribute information pertaining to their workflow processes and outcomes. the benefits of data dashboards are as follows: 1. Ability to quickly visualize graphics of KPIs 2. Ability to identify trends associated with financial impact (typically negative) Ability to measure efficiencies and inefficiencies 3. Ability to make informed decisions' 4. Ability to identify data outliers
Briefly explain data mapping
Data mapping can be defined as the process of linking two distinct or disparate data sources for the purpose of exchanging data or information. It can be referenced as data sharing and/or interoperability. Data mapping requires frequent integrity checks or validation whether through continuous monitoring mechanisms or auditing processes. Accurate mapping should provide uniform, reliable, and complete data, which constitutes its integrity. Challenges of data mapping affect its integrity may be: drop-down pick lists, computer-assisted code assignments, templates that omit important fields, inaccurate workflows, failure to update maps, interface engines, etc. Validity testing in the production environment should be performed routinely to verify that the map is still meeting its intended purpose. Identification of inaccurate mapping results should be investigated for root cause(s) and should be resolved promptly.
Identify the skills necessary for database design
Database design in today's health information market is a critical step needed to manage electronic health data. To be successful in database design, several skills are necessary. Some of these skills will include, at a minimum, the following: 1. Data analysis 2. Data retrieval and reporting 3. Ability to partition data 4. Ability to read data dictionaries 5. Access, Excel, and structured Query language (SQL) knowledge 6. Understanding of database security and access control. 7. Ability to network databases 8. Understanding of if/then formulas 9 Ability to diagram and map out/flowchart database logic 10. Ability to troubleshoot error messages 11. Ability to change database designs when needed 12. Understanding of indexing data to enhance query requests 13. Understanding of backup procedures and restoration of data 14 Ability to ensure data quality, Of these recognized skills, fluency in SQL commands is the most important. SQL commands are used to create, manipulate, and modify data, which is the core of database design and management.
Briefly discuss how mortality rates are calculated and their importance to the healthcare entity
Death rates, also known as mortality rates, are important information in healthcare because they can represent the quality of health services. Death rates are represented in percentages and represent the number of inpatient hospitalizations that ended n death. Dead on arrival cases, abortions (whether therapeutic or spontaneous), and patients who expire in the emergency room (without an order for admission to the hospital) are not included in the rate. The following are various death rates that can be calculated: 1. Gross death rate ( number of deaths of inpatients in a period/number of discharges (including deaths) in the same period). 2. Net death rate (total number of deaths of inpatients-deaths occurring less than 48 hours from admission/total number of discharges (including deaths)- deaths occurring less than 48 hours from admission. 3. Anesthesia death rate (defined as a death occurring while the patient is under anesthesia or caused by anesthetic agents). 4. Postoperative death rate (defined as deaths occurring within 10 days after surgery) 5. Maternal death rate 6. Neonatal death rate
Explain healthcare intelligence
Healthcare intelligence is the coined phrase for the process of organizing and analyzing data at a deeper level. Healthcare intelligence focuses on transforming raw clinical data into meaningful information that can be used for numerous healthcare purposes. The data analysis component of healthcare intelligence processes historical as well as current data, and then analysts can use the results in predictive analysis. Through intelligence methodologies, analysts, also known as clinical informaticists, can drill down through exponential volumes of data to identify even the smallest of errors or compliance issues. In addition to clinical data collected in electronic health records, other types of data privy to healthcare intelligence analysis may include financial data, inventory data, and utility data. The primary objective of care and improve clinical outcomes for patients.
Discuss the functionality of PivotTables and PivotCharts in Excel
In Excel, PivotTables are an excellent tool to summarize data into categories and filter the data in various meaningful ways. A health information management analyst whose responsibility may be data collection and data comparison will find that PivotTables make data collection and comparison easier to complete. Once data are categorized and filtered with a PivotTable, a PivotChart can be created for presentation purposes. To create the PivotTable, choose the best chart style (bar, column) to represent that data, and generate the chart. The PivotTable and PivotCharts can be manipulated on a prescribed timetable (for quarterly reporting purposes) with the addition of new data.
Identify tips for how to best present coding compliance audit data to the administration
In healthcare entities, coding compliance audit data pertaining to accuracy, productivity, and net financial impact will be of interest to administrators on a quarterly basis. PowerPoint slides representing these three components are best displayed as follows: use visual graphs rather than words to communicate pertinent information. For coding accuracy, use trending bar graphs instead of stacked columns or pie charts. Trending bar graphs will provide administrators with a snapshot of how accuracy rates fluctuate from month to month. The trending bar graph should include the expected benchmark standard (95%) graph line as well as inpatient and outpatient coding accuracy rates that may trend above or below the benchmark percentage. For productivity rates, trending should be used with explanation included as to any decrease in volume (vacations, training, vacancies, etc). The net financial impact should be represented from two perspectives: the current quarterly monetary value (positive or negative) and the year-to-date monetary value. These three components of coding compliance audit activities may be used for other purposes, such as cost-benefit analyses, cost justification for additional staff, etc.
Briefly discuss the future of healthcare data integration
In healthcare, data originates in multiple "silos" making it necessary for entities to centralize the data for electronic health purposes; however, the process of data aggregation can be difficult due to complex data sources. Many healthcare entities resort to the process of extract, transform, and load (ETL) for data aggregation. The ETL process involves writing scripts to transform existing data into supported formats. Although the ETL process may bring data together, it does not solve the root problem of disconnected data sources. It is imperative than moving forward, healthcare entities find ways to improve the flow of health information for continuity of patient care. This means health information disparate systems must be linked while maintaining privacy and security. This desired concept moves healthcare entities toward an interoperable information space that enables semantic integration across platforms, regardless of whether the database is an enterprise electronic health record, an isolated departmental data source, or a practice management strategies, and data normalization will be the future of healthcare data integration.
Briefly explain relational databases
In healthcare, multiple different types of data are collected-financial data, and clinical data. The data are collected and stored in databases, with relational databases being one of the most common types used in healthcare. Being knowledgeable of relational database enhances collaborative efforts between health information management (HIM) and information technology (IT) professionals. Input from HIM professionals pertaining to work flow, data definitions, data quality, and health information privacy/confidentiality is needed, and input from IT pertaining to relational database organization is needed to enhance collaborative discussions. Relational databases are organized into relational tables, with rows and columns of recorded data. Each field in the table describes an attribute of a record, such as a patient's last name, first name, medical record number, date of birth etc. One table is also known as a flat file. A relational database consists if two or more related tables that are linked to one another through a unique identifier (medical record number). Through the linkage or multiple related tables, many results may be yielded that can enhance decision-making processes.
Explain measure of central tendency
In statistical analysis, central tendency is known as a single measure that determines the center or middle value of a data set. There are three measures of central tendency: the mean, median, and mode. Health information management professionals should be familiar with these three measures because they are commonly used in healthcare statistics: 1. The mean is the average of the numerical values in a data set. To calculate the mean, the numerical values are summed and then divided by the number of values in the data set. For example, the mean of 2+3+6+4=15/4=3.75. 2. The median is the center value in a distribution list. For example, the median of 1,2,3,4,and 5 is 3 because 50% of the value lies before it and 50% after it in the distribution. 3. The mode is the value that occurs most frequently in the data set. For example, the mode of 1,1,2,3, 4, and 5 would be 1 because it is present more than any other number in the data set.
Identify the excel skills that a health information management (HIM) professional should attain concerning data analytics
In the current state of the health information management (HIM) environment, proficiency in data analytics is essential. HIM professionals must know how to analyze data, and an important tool in data analysis is Excel. An excel spreadsheet is not just a tool to collect data; it is also a tool to tell a story with the data that allows decisions to be made. An in-depth knowledge of excel promotes ease and efficiency of work practices. The following are tasks that HIM professionals and/or data analysts should be proficient in: 1. Data reconciliation through the VLOOKUP function 2. Conditional formatting of cells to control data input 3. Pivot tables and pivot graphs/charts ( to include trending and slicer options) 4. Data filtering options 5. Data sorting functionality 6. Mathematical and/or statistical formula options. 7. Removal of duplicate data functionality.
Discuss techniques on how to identify data anomalies
In the era of big data, it is vitally important to analyze data for anomalies. Anomalies are those unusual occurrences in which the actual result differs from the expected results. Targeted areas for anomaly analysis should include moving averages, trend analysis, statistical control analysis, and basis statistical analysis. Moving averaged can be established to assess a set of data points (every 4 weeks, quarterly) to identify averages in which their trends are skewed significantly. With significant skewing and/or identified outliers, potential data anomalies may be indicated. Trend analysis is a key function to identify data anomalies in need of further investigation. Basic statistical analysis pertaining to standard deviations from the center of distribution is another valuable technique of anomaly analysis.
Identify examples of common reports used for data analytics by health information management professionals and/or healthcare data analysts
In the health information field, data analytics is a common daily task. Obtaining meaningful and relevant data is the primary objective. The following are examples of common data analytical reports generated by health information professionals: 1. Calculation of readmission rates 2. Case mix index 3. Monitoring of complication or comorbidity/major complication or comorbidity rates 4. Monitoring of mortality rates 5. Monitoring data dictionary statistics 6. Trending inpatient and outpatient coding accuracy rates 7. Trending average length of stay 8. Monitoring financial impact of diagnosis-related groups and/or ambulatory payment classification changes. 9. Monitoring adverse drug reactions 10. Monitoring recovery audit contractor appeals 11. Monitoring payer denials With more and more healthcare entities understanding the power if data analysis, the possibilities for additional data monitoring and trending are endless
Identify types of data analysis tools
In the healthcare marketplace, there are many data analysis software packages available for purchase. In Excel, PivotTables are an excellent tool to summarize data into categories and filter that data in various meaningful ways. Excel offers the option of working with frequency tables, which provide a means of summarizing data based upon how often each data element occurs. Excel provides a means of displaying data in an effective manner through the use of tables and charts. In addition to excel, healthcare data analysts can use predictive modeling to analyze historical data for the purpose of identifying patterns upon which to base future decisions. Descriptive and inferential statistics are two types of data analysis, which can be meaningful to the healthcare organization. Types of descriptive and inferential statistics are as follows: central tendency, mean (geometric length of stay) or average (average length of stay), median, mode, percentiles, range, standard deviation, and confidence intervals.
Describe how length-of-stay calculations are determined
Length of stay refers to the number of days a patient is designated as an inpatient- from the date of admission until the date of discharge. This calculation is monitored daily by healthcare entities because it helps to evaluate and manage hospital resources. To compute the length of stay, the date of admission is subtracted from the date of discharge. For example, if a patient was admission on February 2 and then discharged on February 9, the length of stay would be 7 days. If the patient is admitted and discharged the same day the length of stay is counted as 1 day. The average length of stay (ALOS) is a rate consistently monitored by healthcare entities. The ALOS rate is calculated by dividing the total length of stay (also known as discharge days) by the total discharges. For example , 1, 500 patients were discharged during the month of February. The combined length of stay for these patients was 7, 552 days. The ALOS rate would be calculated as follows: 7,552/1,500=5.03 or rounded to 5 days.
Briefly explain best practices in the presentation of data analysis results
Once data are complied and interpreted, the findings must be presented in practical, easy-to-understand terms. To begin the presentation, the reviewer should introduce the purpose of the study, the department studied, and the population studied. The introduction should also include how the data were gathered, the sources of the data, the time frame of data collection, and the names of those who conducted the study. An interpretation section should follow the introduction. Interpretation means to determine the meaning and the significance of the data analysis. Some questions for consideration might be: why did the results turn out the way they did? what are some possible explanations of the data results? Next, a judgement should be made regarding the data results. In other words, do the results have a positive or negative impact and why? What is good or bad about the results? Finally, recommendations should be made in the presentation of data analysis. What should be accomplished as a result of the data analysis, and how will the stakeholders' responsibilities be affected?
Provide tips on how to compute percentages
Percentages are widely used in healthcare statistics; therefore, it is essential to know how to compute percentages. A percentage is defined as the whole divided into one hundred parts. A percentage can be misleading to organizational stakeholders if the total volume (or whole) is 20 or fewer. Therefore, it may be necessary to report only when the volume is greater than 20, which may mean quarterly or annual reporting. A percentage can be computed when a fraction is converted into units of 100. For example, if the fraction is 3/4, then the percentage is computed by dividing 3 by 4, resulting in a decimal of 0.75, which is then converted into a percentage by moving the decimal two places to the right an adding the percent sign (%). For decimal results that are lengthy ( more than 2 decimal places), the healthcare entity/department should have a policy that designates the number of decimal places to round to (21.456% would round to 21.5%). Once a percentage is calculated, it may referred to as rate. In health information management practices, rates are a common calculation, such as death rates, birth rates, readmission rates, etc. It is important to ensure that percent calculations are computed correctly because it can be easy misrepresent the true picture.
Briefly explain how to successfully collect and analyze data for submission to healthcare registries
Prior to the submission of data to a healthcare registry, it is necessary to the healthcare entity to collect and analyze data. The first step in the collection and analysis process is to determine why the data are being collected. Is it for internal purposes such as quality improvement, or is it a state requirement? The next step would be to determine who will represent, when the data will be collected and analyzed. The analysis component must validate the accuracy of the data. A process must be implemented to address any data inconsistencies discovered in the process. Once these steps are planned, implemented, and tested then the data may be submitted to healthcare registries.
Describe the purpose of qualitative analysis of medical records
Qualitative analysis of medical records or electronic health record information is performed by health information personnel. The purpose of quantitative analysis is to identify documentation areas that are incomplete or inaccurate. Examples of documentation deficiencies may be a missing signature on a dictated history and physical report or a progress note, or a missing report entirely such as a discharge summary. Regulatory guidance regarding documentation requirements provide the basis for identifying deficiencies, and health information management departments should always compose a deficiency list based upon external regulations as well as bylaws and medical staff rules and regulations.
Describe the purpose of qualitative analysis of medical records
Qualitative analysis of medical records or health information is the process of identifying deficiencies pertaining to incomplete or inaccurate documentation. The health information management professional analyzing the documentation must understand disease processes in order to identify the deficiencies. For example, a provider may have failed to include the type of congestive heart failure (diastolic versus systolic), which is relevant information when assigning diagnostic codes. The healthcare provider can be queried to obtain clarification or further information, and it is the healthcare provider who makes the final decision that documentation is incomplete or inaccurate. Effective qualitative analysis will, in some cases, impact reimbursement as well as the quality of patient care.
Discuss the components of a qualitative analysis
Quantitative analysis of medical records or electronic health record information is the process of identifying documentation deficiencies. The identified deficiencies must be resolved by the healthcare provider within a time frame of up to 30 days depending upon the type of deficiency. When analyzing for deficiencies, there are certain basic components that must be addressed, as follows: correct patient identification on each form, presence of all required reports (as mandated by the Joint Commission, hospital bylaws, medical staff rules and regulations, etc). and authentication on all entries
Discuss the components of a qualitative analysis
The components of qualitative analysis should include review of the following: 1. Diagnostic statements for completeness ( final diagnoses in the discharge summary should include the principal diagnosis, complications, and any comorbidities that affect the hospitalization) 2. Consistency in documentation by all providers so that conflicting information is avoided ( physician orders for drugs should match the medication administration record) 3. Justification for medical necessity throughout the patient's hospitalization (documentation must justify the course of the patient's entire stay) 4. Presence of informed consent and/or consent to treatment (description of planned operation or description of potential medication side effects.
Briefly discuss problems associated with copy and paste function electronic health records (EHRS)
The copy and paste functionality is an option available in an electronic health record (EHR). Although it offers providers a fast and easy way to document, it also fosters documentation errors, In an EHR, to copy and paste means to reuse all or part of documented text from one part of the record or another part of the record. Documentation errors are of concern because tracking the progress of the patient's condition can be hard to decipher. This may alert auditors to potential noncompliance issues and result in over-and/or under-reimbursement amounts. A reader may misinterpret the extent of a diagnosis if text is copied and pasted over and over throughout the record. For example, documentation of a minor hematoma copied and pasted multiple times may lead the reader/coder to think it is more complex and being consistently monitored throughout the stay, when in reality it was only noted at the time of admission with no further attention being given to it . The coder may then assign a more complex code, resulting in noncompliant diagnosis-related group assignment. This carelessness of duplicate documentation must be vetted by the healthcare entity, and through policies and procedures should be developed to control the process.
Identify the benefits of a data dictionary
The purpose of a data dictionary is to ensure and/or promote data integrity. It provides a clearer understanding of all data elements, aids in locating data, and promotes overall good data management. In addition to these attributes of an effective data dictionary, there are other benefits, as follows: 1. Improved data quality. 2. Improved data reliability. 3. Improved data control. 4. Reduced duplication of data. 5. Data consistency. 6. Effective and efficient data analysis. 7. Better decisions secondary to better data. 8. Improved standardization
Identify ways to limit copy functionality of medical information
The use of copy functionality in a electronic health record is frequently used to ease the physician's burden of redocumenting the same medical information )past medical history) repeatedly. Copy functionality, or duplication of information can be hazardous to patients' health because it can be misleading, inaccurate, and inconsistent and it can increase the risk for medical errors. Therefore, it is necessary for healthcare entities to identify ways to limit the use of copy functionality. Such limitation measures may include: 1. Identification of copied information as originating from a different source. 2. Data transfer restrictions established to prevent copying and pasting information from the sources. 3. Restrictions established that prevent one author reusing another author's information. 4. Implementation of methods aimed at monitoring providers' copying and pasting actions. 5. Implementation of methods aimed at monitoring audit trails.
Briefly explain the process of mapping pathways
Through the use of a code map, one is able to either forward map or backward map. Forward mapping is when the ICD-9 code is available and an ICD-10 code is needed, and backward mapping is the opposite- an ICD-10 code is available, but an ICD-9 code is needed. It is important to understand that general equivalence mappings (GEMS) do not necessarily have a 1:1 match between the two code sets. For example, it is estimated that less than 25% of ICD-9 codes can be mapped to an ICD-10 code. Obviously, there are ICD-9 codes that map to multiple ICD-10 codes, and this known as one-to-many mapping, and in these cases, the multiple ICD-10 codes are more specific than the ICD-9 codes. Because there is a GEM concept for one-to-many mapping, it is important to understand that there is a many-to-on-mapping. In these cases, more than one ICD-9 code is required to provide a match to a single ICD-10 code.
Explain how data analytics may uncover failed patient merges
Through the use of effective data analytics, it is possible for health information management professionals to identify instances wherein patient information may not have merged into one electronic record. For example, if an information merge from disparate system fails, patient information is then kept isolated from a consolidated record. This scenario is a risk to the patient's health and safety. A data analytics program can be developed through Excel PivotTables and/or Access queries to search the database for the unidentified records. For example, newborns are commonly referenced as "Baby Girl" or Baby Boy" at birth. Their names should be updated to their given name at a future time, but if this process is not carried out, then their information could potentially never be matched with future healthcare occurrences. Therefore, data analytics should be used to query for the genetic names and their possible matches with legal names through matching patient identifiers, such as Social Security numbers..
Explain the process of developing a tool that collects statistically valid data
When developing a tool that collects statistically valid data, there are several steps to consider. To begin the process, it is imperative that management considers the resources to use in building the tool. Clinical enterprise data warehouses are an excellent place to start. These warehouses are typically governed by internal clinical informaticists, who have vetted the data and ensure that the data are valid for use. In many cases, the clinical enterprise data warehouses will also be governed by clinical enterprise data warehouses will also be governed by clinical advisory boards, who lend their input and approval of valid data sources. Once a valid clinical enterprise data warehouse or clinical repository is selected and it is determined that its data can be merged into a designed tool, then the development of that tool can commence. A SharePoint content repository is an excellent platform for departments as well as the needs of multiple departments. The SharePoint workflow process is a valuable asset when interdepartmental collaboration is a must. For example, a coding audit tool may be built in SharePoint, using its features of workflow between the audit department, the coding department, and the billing department.
