Assignment Prompt & Instructions
Need to do a Microsoft Access document for my work.
**files (2)**
– ## ASSIGNMENT5COMPLETEWORD.docx
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**## ASSIGNMENT5COMPLETEWORD.docx**
2
**Consideration of MS Access Database Development on Healthcare.**
Name
Instructor
School
Date
**Consideration of MS Access Database Development on Healthcare.**
**Introduction**
The task entailed developing a Microsoft access database of a small healthcare facility, which has just abandoned the use of paper-based records and has introduced the use of a digital system. The database consisted of a Patient Table which had defined fields, inputting of data in Datasheet View, a form creation of the additional records, a grouped report and two queries. I used my last name as the file name which was named as ken.accdb. This was done to simulate a real-world database development within the healthcare sector with a focus on data organization, retrieval, and analysis. The queries, their results, and managerial applications are enumerated below after which I provide a reflection on the process.
**Query 1: Patients Who Visited last year (2019).**
| | | | | | | | |
| — | — | — | — | — | — | — | — |
| **Patient #** | **Last Name** | **First Name** | **Address** | **State** | **Zip Code** | **First Visit** | **Balance** |
| 54783 | Williams | Jack | 62 Smith Lane | MD | 21202 | 2019 | 250 |
| 64589 | Metheny | Alexa | 125 Fairview | MD | 21203 | 2019 | 200 |
| 95874 | Van Wegan | Alison | 100 Quantico | MD | 21204 | 2019 | 350 |
| 23895 | Jackson | Ryan | 2320 Hills Circle | VA | 20147 | 2020 | 325 |
| 96312 | Berstein | Krista | 126 South Street | VA | 20148 | 2020 | 290 |
| 54387 | Wylliams | Karen | 43 Jones Ave. | VA | 22193 | 2021 | 200 |
| 54123 | Ken | Ken | 123 Main St | VA | 22191 | 2021 | 315 |
| 36987 | Booy | Aaron | 21 Fairview Lane Ashburn | VA | 20146 | 2020 | 250 |
| 76213 | McBurney | Colton | 45 Trotters Drive Hagerstown | MD | 21522 | 2022 | 350 |
I used Simple Query Wizard where I chose Patient Table and the following fields; Patient number, last name, and first name. The criteria were First Visit = 2019. The following results were obtained when the query was run:
Patient # 54783: Williams, Jack
Patient # 64589: Metheny, Alexa
Patient # 95874: Van Wegan, Alison
These three patients are the only records that match the first year of visit in 2019.
These results can help the management to analyze the trends of patients in the form of the number of visits per year to aid in resource allocation and strategy planning. In the case of the first visit, the pattern can be seen to predict the future demand of the target group, staffing optimization, and better preventive care programs. As mentioned, the descriptive analytics of historical patient data assists the health care provider to determine the trends in admissions and outcomes, thereby making health care decisions (Turner, 2024). This promotes proactive management which may help save on costs and improve patient engagement by conducting targeted follow-ups.
**Query 2: Patients Owing More Than $300**
| | | | | | | |
| — | — | — | — | — | — | — |
| **Patient #** | **Last Name** | **First Name** | **Address** | **State** | **Zip Code** | **Balance** |
| 95874 | Van Wegan | Alison | 100 Quantico | MD | 21204 | 350 |
| 23895 | Jackson | Ryan | 2320 Hills Circle | VA | 20147 | 325 |
| 54123 | Ken | Ken | 123 Main St | VA | 22191 | 315 |
| 76213 | McBurney | Colton | 45 Trotters Drive Hagerstown | MD | 21522 | 350 |
To answer this query, I selected Patient #, Last Name, First Name, Address, State, Zip Code, and Balance as the table entries that I wished to select in the Simple Query Wizard using the Patient Table. The criteria were Balance > 300. Running the query produced:
Patient # 95874: Van Wegan, Alison, 100 Quantico, MD, 21204, $ 350
Patient # 23895 Jackson, Ryan, 2320 Hills Circle, VA, 20147 $325
Patient # 54123: Ken, Ken, 123 Main St, VA, 22191, $315
Patient # 76213: McBurney, Colton 45 Trotters Drive Hagerstown, MD 21522 350
The following records are used to produce billing statements of the pending balances that are more than 300.
These results can be used in the management of the revenue cycle by non-clinical management groups, including billing and administrative staffs who can focus on collections, the trend of payments, and minimizing accounts receivable. The information provided in claims such as this allows cost analysis and utilization research and demonstrates the payment patterns that may be used to enhance financial stability and make decisions on service pricing or patient assistance programs (CIVHC, 2025). This increases efficiency during the operation without necessarily engaging the clinical staff, and reimbursements are made on time and financial losses minimized.
**Reflection on the Process**
To create the database was not difficult, and after having a blank database as a starting point, the Patient Table would be created using the Design View where the primary key is known as patient number and the properties such as State in uppercase format with the default being IL would be applied. Datasheet View was efficient in data entry during the first seven records and column widths were altered to ease reading. Form Wizard generated a columnar Patient Form making it easier to add the two further records (combining address and city into the Address field where the table did not have a separate City column). The Report Wizard gave a Patient Report (in landscape format, in a collection of steps) by First Visit, slightly Design View manipulated to tweak column widths.
Difficulties were related to the data consistency like in the case of inconsistency between the Address field in the table and the distinct city in the supplementary records, which needed to be concatenated manually. Another problem was that Short Text data type of First Visit (a year) was working, but had restricted advanced date functions. Some of the pitfalls that may arise when using clinics are problems in data entry causing inaccurate queries, problems of scalability with MS Access when dealing with the increasing number of patients, and security issues when not password secured, which may lead to the violation of HIPAA through the unauthorized access of the data.
In its place, I would prefer to suggest a cloud-based electronic health record (EHR) system such as Epic or Cerner instead of MS Access. It has superior integration, real-time work, superior analytics, and compliance capabilities, which minimize manual error and scales bigger datasets. MS Access is an adequate product in small scale prototyping but is not robust enough in long term clinical use where other products offer better data integrity and interoperability.
References
CIVHC. (2025, August 13).
_What to expect with claims data_. CIVHC.org.
[https://civhc.org/2025/08/13/what-to-expect-with-claims-data](https://civhc.org/2025/08/13/what-to-expect-with-claims-data)
Turner, C. (2024, October 16).
_Data analytics in healthcare: Transforming patient care delivery_. Park University.
[https://www.park.edu/blog/data-analytics-in-healthcare-transforming-patient-care-delivery](https://www.park.edu/blog/data-analytics-in-healthcare-transforming-patient-care-delivery)
**ASSIGNMENT5DUEFEB9.docx**
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**ASSIGNMENT5DUEFEB9.docx**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/assignment5duefeb9-docx-10500675)
**## ASSIGNMENT5COMPLETEWORD.docx**
2
**Consideration of MS Access Database Development on Healthcare.**
Name
Instructor
School
Date
**Consideration of MS Access Database Development on Healthcare.**
**Introduction**
The task entailed developing a Microsoft access database of a small healthcare facility, which has just abandoned the use of paper-based records and has introduced the use of a digital system. The database consisted of a Patient Table which had defined fields, inputting of data in Datasheet View, a form creation of the additional records, a grouped report and two queries. I used my last name as the file name which was named as ken.accdb. This was done to simulate a real-world database development within the healthcare sector with a focus on data organization, retrieval, and analysis. The queries, their results, and managerial applications are enumerated below after which I provide a reflection on the process.
**Query 1: Patients Who Visited last year (2019).**
| | | | | | | | |
| — | — | — | — | — | — | — | — |
| **Patient #** | **Last Name** | **First Name** | **Address** | **State** | **Zip Code** | **First Visit** | **Balance** |
| 54783 | Williams | Jack | 62 Smith Lane | MD | 21202 | 2019 | 250 |
| 64589 | Metheny | Alexa | 125 Fairview | MD | 21203 | 2019 | 200 |
| 95874 | Van Wegan | Alison | 100 Quantico | MD | 21204 | 2019 | 350 |
| 23895 | Jackson | Ryan | 2320 Hills Circle | VA | 20147 | 2020 | 325 |
| 96312 | Berstein | Krista | 126 South Street | VA | 20148 | 2020 | 290 |
| 54387 | Wylliams | Karen | 43 Jones Ave. | VA | 22193 | 2021 | 200 |
| 54123 | Ken | Ken | 123 Main St | VA | 22191 | 2021 | 315 |
| 36987 | Booy | Aaron | 21 Fairview Lane Ashburn | VA | 20146 | 2020 | 250 |
| 76213 | McBurney | Colton | 45 Trotters Drive Hagerstown | MD | 21522 | 2022 | 350 |
I used Simple Query Wizard where I chose Patient Table and the following fields; Patient number, last name, and first name. The criteria were First Visit = 2019. The following results were obtained when the query was run:
Patient # 54783: Williams, Jack
Patient # 64589: Metheny, Alexa
Patient # 95874: Van Wegan, Alison
These three patients are the only records that match the first year of visit in 2019.
These results can help the management to analyze the trends of patients in the form of the number of visits per year to aid in resource allocation and strategy planning. In the case of the first visit, the pattern can be seen to predict the future demand of the target group, staffing optimization, and better preventive care programs. As mentioned, the descriptive analytics of historical patient data assists the health care provider to determine the trends in admissions and outcomes, thereby making health care decisions (Turner, 2024). This promotes proactive management which may help save on costs and improve patient engagement by conducting targeted follow-ups.
**Query 2: Patients Owing More Than $300**
| | | | | | | |
| — | — | — | — | — | — | — |
| **Patient #** | **Last Name** | **First Name** | **Address** | **State** | **Zip Code** | **Balance** |
| 95874 | Van Wegan | Alison | 100 Quantico | MD | 21204 | 350 |
| 23895 | Jackson | Ryan | 2320 Hills Circle | VA | 20147 | 325 |
| 54123 | Ken | Ken | 123 Main St | VA | 22191 | 315 |
| 76213 | McBurney | Colton | 45 Trotters Drive Hagerstown | MD | 21522 | 350 |
To answer this query, I selected Patient #, Last Name, First Name, Address, State, Zip Code, and Balance as the table entries that I wished to select in the Simple Query Wizard using the Patient Table. The criteria were Balance > 300. Running the query produced:
Patient # 95874: Van Wegan, Alison, 100 Quantico, MD, 21204, $ 350
Patient # 23895 Jackson, Ryan, 2320 Hills Circle, VA, 20147 $325
Patient # 54123: Ken, Ken, 123 Main St, VA, 22191, $315
Patient # 76213: McBurney, Colton 45 Trotters Drive Hagerstown, MD 21522 350
The following records are used to produce billing statements of the pending balances that are more than 300.
These results can be used in the management of the revenue cycle by non-clinical management groups, including billing and administrative staffs who can focus on collections, the trend of payments, and minimizing accounts receivable. The information provided in claims such as this allows cost analysis and utilization research and demonstrates the payment patterns that may be used to enhance financial stability and make decisions on service pricing or patient assistance programs (CIVHC, 2025). This increases efficiency during the operation without necessarily engaging the clinical staff, and reimbursements are made on time and financial losses minimized.
**Reflection on the Process**
To create the database was not difficult, and after having a blank database as a starting point, the Patient Table would be created using the Design View where the primary key is known as patient number and the properties such as State in uppercase format with the default being IL would be applied. Datasheet View was efficient in data entry during the first seven records and column widths were altered to ease reading. Form Wizard generated a columnar Patient Form making it easier to add the two further records (combining address and city into the Address field where the table did not have a separate City column). The Report Wizard gave a Patient Report (in landscape format, in a collection of steps) by First Visit, slightly Design View manipulated to tweak column widths.
Difficulties were related to the data consistency like in the case of inconsistency between the Address field in the table and the distinct city in the supplementary records, which needed to be concatenated manually. Another problem was that Short Text data type of First Visit (a year) was working, but had restricted advanced date functions. Some of the pitfalls that may arise when using clinics are problems in data entry causing inaccurate queries, problems of scalability with MS Access when dealing with the increasing number of patients, and security issues when not password secured, which may lead to the violation of HIPAA through the unauthorized access of the data.
In its place, I would prefer to suggest a cloud-based electronic health record (EHR) system such as Epic or Cerner instead of MS Access. It has superior integration, real-time work, superior analytics, and compliance capabilities, which minimize manual error and scales bigger datasets. MS Access is an adequate product in small scale prototyping but is not robust enough in long term clinical use where other products offer better data integrity and interoperability.
References
CIVHC. (2025, August 13).
_What to expect with claims data_. CIVHC.org.
[https://civhc.org/2025/08/13/what-to-expect-with-claims-data](https://civhc.org/2025/08/13/what-to-expect-with-claims-data)
Turner, C. (2024, October 16).
_Data analytics in healthcare: Transforming patient care delivery_. Park University.
[https://www.park.edu/blog/data-analytics-in-healthcare-transforming-patient-care-delivery](https://www.park.edu/blog/data-analytics-in-healthcare-transforming-patient-care-delivery)
**ASSIGNMENT5DUEFEB9.docx**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/assignment5duefeb9-docx-10500675)
**## ASSIGNMENT5COMPLETEWORD.docx**
2
**Consideration of MS Access Database Development on Healthcare.**
Name
Instructor
School
Date
**Consideration of MS Access Database Development on Healthcare.**
**Introduction**
The task entailed developing a Microsoft access database of a small healthcare facility, which has just abandoned the use of paper-based records and has introduced the use of a digital system. The database consisted of a Patient Table which had defined fields, inputting of data in Datasheet View, a form creation of the additional records, a grouped report and two queries. I used my last name as the file name which was named as ken.accdb. This was done to simulate a real-world database development within the healthcare sector with a focus on data organization, retrieval, and analysis. The queries, their results, and managerial applications are enumerated below after which I provide a reflection on the process.
**Query 1: Patients Who Visited last year (2019).**
| | | | | | | | |
| — | — | — | — | — | — | — | — |
| **Patient #** | **Last Name** | **First Name** | **Address** | **State** | **Zip Code** | **First Visit** | **Balance** |
| 54783 | Williams | Jack | 62 Smith Lane | MD | 21202 | 2019 | 250 |
| 64589 | Metheny | Alexa | 125 Fairview | MD | 21203 | 2019 | 200 |
| 95874 | Van Wegan | Alison | 100 Quantico | MD | 21204 | 2019 | 350 |
| 23895 | Jackson | Ryan | 2320 Hills Circle | VA | 20147 | 2020 | 325 |
| 96312 | Berstein | Krista | 126 South Street | VA | 20148 | 2020 | 290 |
| 54387 | Wylliams | Karen | 43 Jones Ave. | VA | 22193 | 2021 | 200 |
| 54123 | Ken | Ken | 123 Main St | VA | 22191 | 2021 | 315 |
| 36987 | Booy | Aaron | 21 Fairview Lane Ashburn | VA | 20146 | 2020 | 250 |
| 76213 | McBurney | Colton | 45 Trotters Drive Hagerstown | MD | 21522 | 2022 | 350 |
I used Simple Query Wizard where I chose Patient Table and the following fields; Patient number, last name, and first name. The criteria were First Visit = 2019. The following results were obtained when the query was run:
Patient # 54783: Williams, Jack
Patient # 64589: Metheny, Alexa
Patient # 95874: Van Wegan, Alison
These three patients are the only records that match the first year of visit in 2019.
These results can help the management to analyze the trends of patients in the form of the number of visits per year to aid in resource allocation and strategy planning. In the case of the first visit, the pattern can be seen to predict the future demand of the target group, staffing optimization, and better preventive care programs. As mentioned, the descriptive analytics of historical patient data assists the health care provider to determine the trends in admissions and outcomes, thereby making health care decisions (Turner, 2024). This promotes proactive management which may help save on costs and improve patient engagement by conducting targeted follow-ups.
**Query 2: Patients Owing More Than $300**
| | | | | | | |
| — | — | — | — | — | — | — |
| **Patient #** | **Last Name** | **First Name** | **Address** | **State** | **Zip Code** | **Balance** |
| 95874 | Van Wegan | Alison | 100 Quantico | MD | 21204 | 350 |
| 23895 | Jackson | Ryan | 2320 Hills Circle | VA | 20147 | 325 |
| 54123 | Ken | Ken | 123 Main St | VA | 22191 | 315 |
| 76213 | McBurney | Colton | 45 Trotters Drive Hagerstown | MD | 21522 | 350 |
To answer this query, I selected Patient #, Last Name, First Name, Address, State, Zip Code, and Balance as the table entries that I wished to select in the Simple Query Wizard using the Patient Table. The criteria were Balance > 300. Running the query produced:
Patient # 95874: Van Wegan, Alison, 100 Quantico, MD, 21204, $ 350
Patient # 23895 Jackson, Ryan, 2320 Hills Circle, VA, 20147 $325
Patient # 54123: Ken, Ken, 123 Main St, VA, 22191, $315
Patient # 76213: McBurney, Colton 45 Trotters Drive Hagerstown, MD 21522 350
The following records are used to produce billing statements of the pending balances that are more than 300.
These results can be used in the management of the revenue cycle by non-clinical management groups, including billing and administrative staffs who can focus on collections, the trend of payments, and minimizing accounts receivable. The information provided in claims such as this allows cost analysis and utilization research and demonstrates the payment patterns that may be used to enhance financial stability and make decisions on service pricing or patient assistance programs (CIVHC, 2025). This increases efficiency during the operation without necessarily engaging the clinical staff, and reimbursements are made on time and financial losses minimized.
**Reflection on the Process**
To create the database was not difficult, and after having a blank database as a starting point, the Patient Table would be created using the Design View where the primary key is known as patient number and the properties such as State in uppercase format with the default being IL would be applied. Datasheet View was efficient in data entry during the first seven records and column widths were altered to ease reading. Form Wizard generated a columnar Patient Form making it easier to add the two further records (combining address and city into the Address field where the table did not have a separate City column). The Report Wizard gave a Patient Report (in landscape format, in a collection of steps) by First Visit, slightly Design View manipulated to tweak column widths.
Difficulties were related to the data consistency like in the case of inconsistency between the Address field in the table and the distinct city in the supplementary records, which needed to be concatenated manually. Another problem was that Short Text data type of First Visit (a year) was working, but had restricted advanced date functions. Some of the pitfalls that may arise when using clinics are problems in data entry causing inaccurate queries, problems of scalability with MS Access when dealing with the increasing number of patients, and security issues when not password secured, which may lead to the violation of HIPAA through the unauthorized access of the data.
In its place, I would prefer to suggest a cloud-based electronic health record (EHR) system such as Epic or Cerner instead of MS Access. It has superior integration, real-time work, superior analytics, and compliance capabilities, which minimize manual error and scales bigger datasets. MS Access is an adequate product in small scale prototyping but is not robust enough in long term clinical use where other products offer better data integrity and interoperability.
References
CIVHC. (2025, August 13).
_What to expect with claims data_. CIVHC.org.
[https://civhc.org/2025/08/13/what-to-expect-with-claims-data](https://civhc.org/2025/08/13/what-to-expect-with-claims-data)
Turner, C. (2024, October 16).
_Data analytics in healthcare: Transforming patient care delivery_. Park University.
[https://www.park.edu/blog/data-analytics-in-healthcare-transforming-patient-care-delivery](https://www.park.edu/blog/data-analytics-in-healthcare-transforming-patient-care-delivery)
**ASSIGNMENT5DUEFEB9.docx**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/assignment5duefeb9-docx-10500675)