Background: Older adults face an increased risk for falling and resulting injury due to age-related physiological changes (Sharif et al., 2018). Effective fall risk screening programs in the primary care setting may be a promising approach to reduce the incidence of falls within this population (Siegrist et al., 2016). The use of technology can be beneficial in supporting care delivery and further reducing the risk.
Purpose: This article discusses the implementation of a fall risk screening and reduction program in a primary care setting. The program uses resources of the Centers for Disease Control and Prevention’s (CDC) Stopping Elderly Accidents, Deaths & Injuries (STEADI) Initiative and the development of an Audio Computer-Assisted Self-Interview (ACASI). This study aimed to increase community-dwelling older adult safety by identifying and reducing fall risk.
Clinical Relevance: ACASI is an effective alternative to traditional interview methods due to its speed in data capture and the potential for increased accuracy of self-reported data. Introducing ACASI technology in nursing practice is an innovative approach to collecting patient data in a manner that limits physical interaction, reduces bias, and promotes safe social practices, especially when considering the current COVID-19 pandemic.
Approach: Participants were recruited from an outpatient facility and consented to participate in the study. Chart reviews were conducted to identify eligible participants. To determine the baseline fall risk scores, questionnaires were presented to participants in the form of an ACASI. This technology was used to communicate the assessment questions and facilitate data collection. Evidence-based informational materials were administered to participants to provide education on fall prevention safety and limiting fall risk. Eight weeks after receiving education on fall risk reduction behavioral and environmental modifications, a follow-up, ACASI- formatted questionnaire was administered to assess for a change in fall risk scores.
Results: The research findings demonstrated a significant reduction in fall risk scores (t (40) = -2.220, p =.032) from pre- to post-assessment. Overall fall risk scores among the participants decreased by 43% within a 12-week timeframe.
One in four adults age 65+ report falling, resulting in over 50% of all injury-related deaths (Haddad et al., 2018). Individuals who suffer from falls are not only predisposed to injury and untimely death, but also decreased mobility, decreased independence, hospitalization, and nursing home placement (Phelan et al., 2016). The cost to medically treat a fall is approximately $10,000 in direct fees (Dellinger, 2017).
Costs associated with caring for fall-related injuries exceed $30 billion each year (Phelan et al., 2016). The National Patient Safety Goals, established by The Joint Commission (TJC) in 2003, is a safety and quality improvement program to help healthcare organizations target concerning areas of need (The Joint Commission, 2022a). Sentinel Event Alerts, which warn healthcare organizations about risks to patient safety, are included with the safety goal reports. Injurious patient falls have regularly ranked in the top 10 among the TJC’s list of Sentinel Events (The Joint Commission, 2022b).
Most falls among older adults result from a combination of risk factors. Approaches to assess and manage modifiable risk factors have been identified as effective interventions for individuals at risk of falling (Phelan et al., 2016). Primary care practitioners can play a key role in identifying and reducing fall risk among patients by identifying and discussing risk factors during regular office visits. The physiologic changes and high incidence of falls in the elderly make it necessary to conduct regular fall risk assessments and interventions among this population (Siegrist et al., 2016). Among the leading fall risk assessment resources that have recently been developed is the Centers for Disease Control and Prevention’s (CDC) STEADI (Stopping Elderly Accidents, Deaths & Injuries) Initiative (Centers for Disease Control and Prevention, 2017c). STEADI offers healthcare providers a standardized approach to conduct fall risk screenings, assessments, and interventions for older adults (Howland et al., 2018).
Technology can assist in screening patients for fall risk in the form of an Audio Computer-Assisted Self-Interview (ACASI). An ACASI-administered survey is a method of data collection that allows participants to complete interviews on their own without the presence of a human interviewer. Questions and response options are displayed as digital text on a personal electronic device and read aloud to participants. The participants listen to the pre-recorded questions and respond by selecting their answers directly on the screen. ACASI is believed to improve the quality of data collection by minimizing data entry errors. Additional benefits of using ACASI, as opposed to traditional survey questionnaires, include increased privacy for participants, accessibility for illiterate participants, reduced staff time for interviewing, and increased data validity for sensitive questions (Kane et al., 2016). In this study, an ACASI was developed and administered to participants in tandem with CDC STEADI (2017c) resources, as part of a fall risk screening and reduction program to identify and reduce the risk of falling among community-dwelling older adults.
A literature review to identify high-quality evidence to support the purposes of this project was performed using the John Hopkins Nursing Evidence-Based Practice (JHNEBP) Evidence Level and Quality Guide (Figure 1). According to the JHNEBP, the highest levels of evidence are randomized controlled trials (RCTs) or systematic reviews of RCTs (John Hopkins Health System, n.d.). Only a few studies have been conducted to identify gaps in fall risk assessments in the primary care setting. In a systematic review performed by Howland et al., (2018), a random sample survey of Colorado primary care physicians and their fall prevention practices for older adults found that only 8% of providers reported fall prevention practices based on recommended guidelines. Common reasons cited were lack of time, more urgent medical problems, and lack of educational materials. Less than 40% of New York primary care providers inquire if their older adult patients have fallen in the last year. Less than 16% conduct standardized functional assessments with their older patients at least once a year.
A systematic review performed by Guirguis-Blake et al., (2018), affirmed that within primary care populations, fall prevention interventions can include addressing modifiable fall risk factors such as gait, balance, medication adverse effects, and environmental factors. Exercise is a critical component of fall prevention in the elderly because of its ability to strengthen gait, balance and flexibility, among other factors. The systematic review performed by Ng et al., (2019), found that exercise activities such as resistance training and Tai Chi can be beneficial in reducing fall risk among the elderly. Effects of certain medications can place patients, especially the elderly, at risk for falls. Side effects include dizziness, blurred vision, confusion, and orthostatic hypotension. Guidance from the CDC (2017c) and a Cochrane systematic review supports eliminating or exchanging these medications when possible as a modifiable fall risk factor. Hazards within the home are a common environmental fall risk. Evidence has shown that modifications within the home environment, such as reducing tripping hazards, are effective in reducing falls (Dellinger, 2017). An experimental study by Lohman et al., (2017), found that the CDC’s STEADI fall risk toolkit has great potential for measuring fall risk among older adults and informing of population-based fall prevention initiatives. The STEADI clinical fall risk screening tool has been identified as a validated measure for predicting future fall risk.
A gap analysis was performed to assess the need for conducting fall risk assessments on patients aged 65 and older within a primary care practice. A gap in the current state of the practice pertaining to fall risk identification was determined to be a lack of appropriate screening for fall risk in the elderly. This lack of screening was determined to be a context issue within this primary care practice, which appeared to have no formal process to address the problem.
The quality improvement model that was used as a framework for this study is the Plan-Do-Study-Act (PDSA) model because of its proven benefits in process improvement (Coury et al., 2017). The purpose of the PDSA model is to test a change by developing a plan, carrying out the plan, analyzing the results, and making refinements based upon what was learned from implementation (Institute for Healthcare Improvement, 2021). This method was adhered to in executing this project. Usage of the PDSA model has the potential to facilitate clinics and primary care practices in integrating research-based interventions into everyday care processes (Coury et al., 2017).
The JHNEBP Evidence Level and Quality Guide adequately assisted this initiative in translating evidence into clinical practice. The guide was chosen because of its goal of ensuring that best practices are adequately incorporated into patient care (Dang & Dearholt, 2018). The guide is composed of three steps: practice question, evidence and translation (John Hopkins Health System, n.d.). For this project, this model was essential in gathering pertinent literature, appraising the evidence to identify the highest quality that will aid in answering the project question, and synthesizing the evidence to support the proposed practice change (Howe & Close, 2016).
Florence Nightingale’s Environmental Theory of Nursing best supported this study. Nightingale believed that providing a suitable environment directly affected the health and healing of a patient (Thompson & Barcott, 2017). When targeting fall risk factors for this study, there was a primary focus on risk factors that can induce the risk of mechanical falls within a patient’s personal home environment, such as toileting needs and the use of home furniture for balance aids. Thus, Florence Nightingale’s theory captured the essence of the project to improve care delivery and impact patient outcomes by manipulating the patient’s environment.
Setting and Sample
After receiving appropriate approval from the Institutional Review Board (#1749213-2), participants of the fall risk screening and reduction program were recruited via purposive sampling from a primary care practice in Washington, D.C., and consented to participate in the study. The practice is a single practitioner operation in which approximately 40-45% of the patients are age 65 or older. Chart reviews were conducted to identify participants that were within the required age range. All registered patients of the practice age 65 and older were invited to participate in the study. Invitations to participate were distributed via email or in person during office visits by either the project investigator or office staff who were trained on inclusion criteria.
Inclusion criteria for participants were age 65 and older, access to the internet, able to read English, and registered as a new patient within the 30-day invitation window that met the age criteria. Exclusion criteria included patients less than age 65, registration as a new patient outside of the invitation window, unable to read English, or no internet access. The study ran for approximately 12 weeks from June 2021 to August 2021. A total of 47 participants were recruited at the start of the study; 87% finished the study, for a total of 41 participants.
Data on gait, balance, medication, ambulation, and home environment were collected from participants. Assessment questions followed the format of the “Check Your Risk for Falling” fall risk questionnaire (FRQ) developed by the Greater Los Angeles VA Geriatric Research Education Clinical Center and associates as a part of the STEADI toolkit (Figure 2). The FRQ is a 12-item questionnaire used to screen older adults at risk for falls. Scores range from 0-14, with a score of 4 or higher indicating a potential risk for falling (Centers for Disease Control and Prevention, 2017b). The FRQ’s validity and reliability have been evaluated. The FRQ has a moderate strong correlation with the Timed Up and Go (TUG) test, the Berg Balance Scale (BBS), and the Five Times Sit-To-Stand (5TsTs) test (r= 0.535 to 0.690, p<0.001). Test-retest reliability is high, Kappa=1. Cronbach’s alpha= 0.936 for internal consistency. The TUG, BBS, and 5TsTs are evidence-based best practice tests to measure balance, mobility and muscle strength, especially among the elderly. A moderately strong correlation aligns the FRQ with the above tests as a best practice tool to measure fall risk (Kitcharanant et al., 2020).
The ACASI was developed using Google Forms software to deliver the assessment questions of the FRQ and capture the responses. Each question of the FRQ was transposed to a custom-built electronic assessment with Yes or No radio buttons to select the desired response. The project investigator audio-recorded each question and embedded the clip above the written question, allowing participants the option to play the audio clip to hear a verbal reading of each question. Once created, a link to the assessment form was generated for distribution purposes. The benefits of software programs such as Google Forms include their global reach, minimal cost, and ease of analysis (Andrade, 2020). ACASI has been linked with reducing some of the bias that may occur during face-to-face interviews and is beneficial in improving data collection (Falb et al., 2016).A live demonstration in-service provided to the staff (physician, nurses) detailed how to navigate the ACASI and how to distribute it to eligible patients.
To determine their current fall risk scores. eligible patients were invited during a 30-day window to take the ACASI FRQ (Pre-FRQ) on a tablet device in office or at home via a link to the assessment. Participants who chose to take the FRQ questionnaire in the office were administered educational materials after completion to read and incorporate into their daily activities at home. If the FRQ was taken outside of the office, educational materials were emailed with instructions to first complete the FRQ and then read materials and incorporate. Educational materials were brochures promoted as a part of the same STEADI toolkit; “What You Can Do to Prevent Falls” (Figure 3) (Centers for Disease Control and Prevention, 2017d). and “Check for Safety: A Home Fall Prevention Checklist for Older Adults” (Figure 4) (Centers for Disease Control and Prevention, 2017a). The next 30 days were dedicated to receiving responses to the Pre-FRQ, allowing time for participants to read the educational materials and make the appropriate fall prevention adjustments. Responses were scored, with a higher score representing a higher fall risk. After the first 60 days, a follow-up ACASI FRQ (Post-FRQ) was emailed to all original participants. Reminder emails to participate in the study were sent out twice during each invitation window. The final 30 days were dedicated to receiving responses to the Post-FRQ. Responses were again scored to determine fall risk.
The FRQ questionnaire responses were electronically collected in the Google Forms software. The data were analyzed via SPSS software for descriptive statistics, correlation analysis and statistical significance. The number of participants that scored a 4 or higher (Fall Risk) was calculated for both the Pre and Post groups to determine the decrease (percentage) in participant’s fall risk at the conclusion of the study. Descriptive statistics were used to calculate the percentage mean for both the Pre-FRQ and Post-FRQ groups. The first project goal was to measure a significant difference (p<0.05) between the average Pre and Post fall risk scores. Statistical t-test was performed to determine if the difference was by chance or statistically significant. A significant difference would suggest the project was effective. The second project goal was to measure a 30% decrease in participants scoring 4 or greater by project completion.
A total of 47 participants took part in the study for the pre-assessment phase. Of the initial 47 participants, 41 returned to take part in the study for the post-assessment phase. A response of No on the FRQ carried a weight of 0 points. A response of Yes could range between 1-2 points. The average fall risk score for the Pre-FRQ group was 3.91 (SD=3.380). The average fall risk score for the Post-FRQ group was 2.95 (SD=2.765). Pre-assessment scores ranged from 0-12. Post-assessment scores ranged from 0-11. Of the Pre-FRQ group, 22 of 47 participants scored a 4 or higher on the FRQ, resulting in 46.8% of the sample considered to be at risk for falling (see Table 1). Of the Post-FRQ group, 11 of 41 participants scored a 4 or higher on the FRQ, resulting in a decrease to 26.8% of the sample at risk for falling (see Table 2). See Figure 5 for a graphical comparison of the Pre and Post FRQ scores.
Questions 1 and 2 on the FRQ saw a small increase in Yes responses for “I have fallen in the past year” (Q1) and “I use or have been advised to use a cane or walker to get around safely” (Q2) among the Pre- and Post-FRQ groups (Table 3). The difference is not significant (p = .844) and likely attributed to the decreased sample size from Pre to Post. All other questions saw a decrease in Yes responses and an increase in No responses (see Figure 6).
Statistical t- Test
A single-sample t-test that compared the mean Post-FRQ group score to the mean Pre-FRQ group score was conducted. A significant difference was found (t (40)= -2.220, p=.032). The Post-FRQ mean of 2.95 (sd=2.765) was significantly different than the Pre-FRQ population mean of 3.91 (sd=3.380), indicating that this considerable difference is not due to chance (Table 4).
INSERT Table 4: Single-sample t-test comparing Pre-FRQ and Post-FRQ means
A Pearson’s correlation coefficient (r) was conducted between several questions within the FRQ that found a moderate positive correlation:
These correlations suggest that:
The fall risk screening and reduction program decreased the amount of participants scoring 4 or greater by 20% during the 12-week timeframe. The average fall risk score reduced from 3.91 to 2.95. This difference was found to be significant at (p < 0.05). Correlations found among variables provide insight to the possibility that adjusting one area of fall risk may aid in adjusting another area of fall risk. The decrease in the Post-FRQ Yes responses suggest that the educational materials were successful in educating participants on behaviors that can reduce their risk of falling. The ACASI approach can be beneficial beyond this study to reach, screen and educate the elderly and non-elderly in the current COVID age because ACASI allows for certain health assessments to be conducted while limiting unnecessary physical contact.
A smaller than intended sample size was a limitation of this study. The sample size further reduced at the post-assessment group although reminder emails were issued. This reduced sample size could have introduced some bias into the study findings. Potential barriers could be technological illiteracies that prevented some participants from taking part in the study. In addition, the project length may have also played a role in participant engagement. Future iterations of this project will seek to expand the timeframe to allow for a larger sample size and potential increased engagement.
Implementation of the fall risk screening and reduction program was found to be successful in decreasing the risk of falls among primary care older adults (65+). Patient-reported data demonstrated an increased knowledge of modifiable fall risk factors and high fall risk behaviors. No incentives were offered for participation in this study; however, future studies can consider offering incentives to increase participation and adherence. Given the different needs and availability of participants, the use of ACASI technology to administer the questionnaires was pragmatic and favorable among staff and patients. The study results suggest that the CDC’s STEADI toolkit (2017c) is effective in reducing fall risk in the elderly, and technology can be used as an efficient way to facilitate the process.
Clinical Relevance Statement
The ability for ACASI to provide accurate self-reported data and its capacity for immediate data download compared to traditional interview methods make ACASI an effective alternative in the clinical setting (Kane et al., 2016). The introduction of the ACASI tool in nursing practice has the potential to revolutionize the way care is administered for many conditions within healthcare facilities, with notable consideration given to the COVID-19 pandemic. In addition to reducing physical interaction and improving data collection, ACASI reduces a patient’s pressure and anxiety compared to answering a provider’s questions face-to-face (Falb et al., 2017). The use of electronic data collection via ACASI is pertinent to advanced practice nurses and nursing informatics because it promotes greater transparency in patient-reported data in a manner that is timely and that reduces bias. Patients will benefit from increased quality care delivery and better health outcomes.
Powered by the HIMSS Foundation and the HIMSS Nursing Informatics Community, the Online Journal of Nursing Informatics is a free, international, peer reviewed publication that is published three times a year and supports all functional areas of nursing informatics.
Andrade C. (2020). The limitations of online surveys. Indian Journal of Psychological Medicine, 42(6), 575–576. https://doi.org/10.1177/0253717620957496
Centers for Disease Control and Prevention. (2017a). Check for safety: A home fall prevention checklist for older adults [Brochure]. https://www.cdc.gov/steadi/pdf/STEADI-Brochure-CheckForSafety-508.pdf
Centers for Disease Control and Prevention. (2017b). Stay independent: Learn more about fall prevention [Brochure]. https://www.cdc.gov/steadi/pdf/STEADI-Brochure-StayIndependent-508.pdf
Centers for Disease Control and Prevention. (2017c). STEADI Older Adult Fall Prevention Initiative. https://www.cdc.gov/steadi/pdf/STEADI-Brochure-StayIndependent-508.pdf
Centers for Disease Control and Prevention. (2017d). What you can do to prevent falls. [Brochure]. Retrieved from https://www.cdc.gov/steadi/pdf/STEADI-Brochure-WhatYouCanDo-508.pdf
Coury, J., Schneider, J. L., Rivelli, J. S., Petrik, A. F., Seibel, E., D'Agostini, B., Taplin, S. H., Green, B. B., & Coronado, G. D. (2017). Applying the plan-do-study-act (PDSA) approach to a large pragmatic study involving safety net clinics. BMC Health Services Research, 17(1), 411. https://doi.org/10.1186/s12913-017-2364-3
Cronk, B.C. (2020). How to use SPSS: A step-by-step guide to analysis and interpretation. Routledge.
Dang, D. & Dearholt, S.L. (2018). Johns Hopkins nursing evidence-based practice: models and guidelines (3rd ed.). https://www.issuhub.com/view/index/6970.
Dellinger A. (2017). Older adult falls: Effective approaches to prevention. Current Trauma Reports, 3(2), 118–123. https://doi.org/10.1007/s40719-017-0087-x
Falb, K., Tanner, S., Asghar, K., Souidi, S., Mierzwa, S., Assazenew, A., Bakomere, T., Mallinga, P., Robinette, K., Tibebu, W., & Stark, L. (2017). Implementation of audio-computer assisted self-interview (ACASI) among adolescent girls in humanitarian settings: feasibility, acceptability, and lessons learned. Conflict and Health, 10, 32. https://doi.org/10.1186/s13031-016-0098-1
Guirguis-Blake, J. M., Michael, Y. L., Perdue, L. A., Coppola, E. L., & Beil, T. L. (2018). Interventions to prevent falls in older adults: updated evidence report and systematic review for the US preventive services task force. JAMA, 319(16), 1705–1716. https://doi.org/10.1001/jama.2017.21962
Haddad, Y. K., Bergen, G., & Luo, F. (2018). Reducing fall risk in older adults. The American Journal of Nursing, 118(7), 21–22. https://doi.org/10.1097/01.NAJ.0000541429.36218.2d
Howe, C.J., & Close, S. (2016). Be an expert: Take action with evidence-based practice. Journal of Pediatric Nursing, 31(3), 360-362. https://doi.org/10.1016/j.pedn.2016.02.010
Howland, J., Hackman, H., Taylor, A., O'Hara, K., Liu, J., & Brusch, J. (2018). Older adult fall prevention practices among primary care providers at accountable care organizations: A pilot study. PloS One, 13(10), e0205279. https://doi.org/10.1371/journal.pone.0205279
Institute for Healthcare Improvement. (2021). Science of Improvement: Testing changes. http://www.ihi.org/resources/Pages/Tools/PlanDoStudyActWorksheet.aspx
John Hopkins Health System. (n.d.). John Hopkins evidence-based practice model. https://www.hopkinsmedicine.org/evidence-based-practice/ijhn_2017_ebp.html
Kane, J. C., Murray, L. K., Sughrue, S., DeMulder, J., Skavenski van Wyk, S., Queenan, J., Tang, A., & Bolton, P. (2016). Process and implementation of audio computer assisted self-interviewing (ACASI) assessments in low resource settings: a case example from Zambia. Global Mental Health (Cambridge, England), 3, e24. https://doi.org/10.1017/gmh.2016.19
Kitcharanant, N., Vanitcharoenkul, E., & Unnanuntana, A. (2020). Validity and reliability of the self-rated fall risk questionnaire in older adults with osteoporosis. BMC Musculoskeletal Disorders, 21(1), 757. https://doi.org/10.1186/s12891-020-03788-z
Lohman, M. C., Crow, R. S., DiMilia, P. R., Nicklett, E. J., Bruce, M. L., & Batsis, J. A. (2017). Operationalisation and validation of the stopping elderly accidents, deaths, and injuries (STEADI) fall risk algorithm in a nationally representative sample. Journal of Epidemiology and Community Health, 71(12), 1191–1197. https://doi.org/10.1136/jech-2017-209769
Ng, C., Fairhall, N., Wallbank, G., Tiedemann, A., Michaleff, Z. A., & Sherrington, C. (2019). Exercise for falls prevention in community-dwelling older adults: trial and participant characteristics, interventions and bias in clinical trials from a systematic review. BMJ Open Sport & Exercise Medicine, 5(1), e000663. https://doi.org/10.1136/bmjsem-2019-000663
Ogbuokiri, U. & Ahaghotu, E. (2022). Using an audio computer-assisted self-interview (ACASI) to screen older adults for fall risk in an outpatient primary care setting. Online Journal of Nursing Informatics (OJNI), 26 (2), https://www.himss.org/resources/online-journal-nursing-informatics
Phelan, E. A., Aerts, S., Dowler, D., Eckstrom, E., & Casey, C. M. (2016). Adoption of evidence-based fall prevention practices in primary care for older adults with a history of falls. Frontiers in Public Health, 4, 190. https://doi.org/10.3389/fpubh.2016.00190
Sharif, S. I., Al-Harbi, A. B., Al-Shihabi, A. M., Al-Daour, D. S., & Sharif, R. S. (2018). Falls in the elderly: assessment of prevalence and risk factors. Pharmacy Practice, 16(3), 1206. https://doi.org/10.18549/PharmPract.2018.03.1206
Siegrist, M., Freiberger, E., Geilhof, B., Salb, J., Hentschke, C., Landendoerfer, P., Linde, K., Halle, M., & Blank, W. A. (2016). Fall prevention in a primary care setting. Deutsches Arzteblatt International, 113(21), 365–372. https://doi.org/10.3238/arztebl.2016.0365
The Joint Commission. (2022a). National patient safety goals. https://www.jointcommission.org/standards/national-patient-safety-goals/
The Joint Commission. (2022b). Sentinel Event data released for 2021. https://www.jointcommission.org/resources/news-and-multimedia/newsletters/newsletters/joint-commission-online/march-9-2022/sentinel-event-data-released-for-2021/
Thompson, M. R., & Schwartz Barcott, D. (2017). The concept of exposure in environmental health for nursing. Journal of Advanced Nursing, 73(6), 1315–1330. https://doi.org/10.1111/jan.13246
Utokia Ogbuokiri, DNP, MSN, RN
Dr. Utokia Ogbuokiri is the Global Health Informatics Nurse for Intel Corporation. She specializes in data analysis for the capture of occupational injury and illness metrics and measuring the effectiveness of occupational health programs. She has served as subject matter expert for full life cycle end-to-end implementations of SaaS clinical information systems and Cerner EHR software. In addition, Dr. Ogbuokiri has served as EHR educator for inpatient nurses and nursing assistants for Medstar Health. Prior to her role in informatics, she worked as a cardiac and emergency care clinical nurse. Her professional focus is the use of data and innovative healthcare technologies to drive improvements in quality of care and bridge the chasm of health disparities. Dr. Ogbuokiri holds a BA in biology from the University of Maryland – Baltimore County, a BSN from Stratford University, an MSN in nursing informatics from the University of South Alabama and a DNP in nursing informatics from the University of South Alabama.
Amy Campbell, DNP, RN
Amy Campbell, DNP, RN is an Assistant Professor to undergraduate and Masters students in Health Informatics at the University of South Alabama, and was instrumental in the program's formation. Dr. Campbell’s research focuses on utilizing data mining techniques to identify needs, creating technological tools to improve the healthcare environment, and enhancing computer and health literacy for nurses and patients alike. She currently holds a patent for measuring nursing workload based on the nurse's natural strengths. She previously taught Masters and Doctorate students in the Nursing Informatic program in the College of Nursing at USA. Before entering academia, Dr Campbell worked in clinical practice as a Hospice Nurse and Wound Care Specialist. She earned her Doctorate and Masters of Nursing Informatics from the University of South Alabama (USA) and her bachelor’s degree in Nursing from Tennessee Technological University. Her research has been published in numerous journals and conference proceedings.
Ezenwanyi Ahaghotu, NMD, MS
Dr. Ahaghotu earned her doctor of naturopathic medicine degree from Southwest College of Naturopathic Medicine and Health Sciences in Tempe, Arizona. She operates her own private practice in which she works closely with patients to help explain, educate, reinforce and coach wellness initiatives according to individual goals and tailored self-help plans. Dr. Ahaghotu is a strong believer of integrative medicine and is enthusiastic about educating individuals who are seeking alternative methods to improve their health and lifestyle. In addition to her naturopathic medical degree, Dr. Ahaghotu also holds a Bachelor of Science degree in chemistry from Lincoln University and Master of Science degree in pharmaceutics from Florida A&M University.