• An Alpha-1 Antitrypsin Deficiency Screening Tool to Identify Patients at Risk

      Fitzpatrick, Heather N.; Scheu, Karen (2020-05)
      Problem & Purpose: Early identification of Alpha-1 Antitrypsin Deficiency (AATD) could prevent widespread pathological destruction of the lung parenchyma, possibly delaying time to death. On average AATD patients experience a diagnostic delay of six years and have to consult three physicians until diagnosis is established. With the implementation of established guidelines, unidentified individuals at risk for AATD may be identified and treated earlier to prevent premature death. The purpose of this quality improvement project is to implement a clinical practice guideline-based screening tool to identify at-risk patients for AATD at a rural primary care practice where the patient population is at risk. Methods: An evidence –based screening tool was administered for every patient with an appointment at the primary care practice. Patients who met inclusion criteria based upon personal or family medical history were screened for AATD using an evidenced-based tool during routine provider visits. Patient’s found to be at-risk with a were offered testing for AATD in the practice. Results: A total of 235 patients were screened over 12 weeks with an overall screening rate of 96.8% after implementation of the screening tool. 11% of patients screened were found to be atrisk for AATD, 35% were male and 65% were female. There was no difference between gender and being at-risk, p <.0005. Conclusion: Improvements of identifying patients at risk for AATD were accomplished by implementing staff education as well as paper evidenced-based screening tools during each appointment resulting in with 11% positive results.
    • Implementing Medicare Annual Wellness Visits with a Health Risk Assessment in Primary Care

      Owens, Tiffany N.; Bundy, Elaine (2019-05)
      Background: Within the primary care setting, there is a deficiency of comprehensive, personalized treatment care plans that identify modifiable risk factors and endorse preventive care. The Medicare annual wellness visit presents an opportunity for patients aged 65 years and older to identify, plan, and optimally manage chronic health conditions and increase preventative care. The health risk assessment, which is part of the annual wellness visit, is intended to identify health behaviors and risk factors that can be discussed with the patient and utilized to collaboratively create a personalized prevention plan that aims to reduce risk factors and related diseases. Local Problem: In a small, single practitioner primary care office, there was a low performance of completion of annual wellness visits with the Medicare population and lack of a consistent method to assess health risks within this population. This practice serves a Medicare population of greater than 300 patients yet only billed a total of 39 annual wellness visits in 2017 and 15 in 2018. The purpose of this quality improvement Doctor of Nursing Practice project was to increase the number of Medicare annual wellness visits, which included the use of a Health risk assessment in a primary care practice, for Medicare patients aged 65 years and older with chronic health conditions. Interventions: The project was implemented over a 14 week period. Mail and telephonic outreach were conducted to all eligible Medicare patients. For beneficiaries with preexisting appointments, annual wellness visits were added to the appointments. Health risk assessments were mailed to the patient after the appointment was scheduled with instructions to complete prior and bring to the scheduled appointment. Health risk assessments were collected when the patient checked in for the scheduled appointment. Results: The percentage of annual wellness visits completed or not completed (among eligible patients) during the pre- intervention and post- intervention was determined by dividing the total number of eligible patients who completed their annual wellness visits by the total number of eligible patients. At the conclusion of the project, there was a 23.7%, or five- fold- increase in the annual wellness visits completed, which is statistically significant. Post- intervention chart audits revealed health risk assessments in 100% of the charts when an annual wellness visit was completed. Conclusions: Annual wellness visits can be integrated successfully in a busy outpatient primary care practice within the time allocated for office visits. Completion of annual wellness visits increased significantly over the project two month implementation timeframe. A tracking tool revealed a higher capture rate when annual wellness visits were scheduled with pre- arranged office visits. Patient and provider participation in the process increased referrals for preventative screenings and vaccinations. The annual wellness visit also has the opportunity to increase practice revenue gained from Medicare reimbursement and increased relative value units.
    • Testing a 30-day readmission risk calculator in a veteran population with heart failure: A pilot study

      Gannuscio, Jacqueline R. (2012)
      index HF hospitalization between September 2007 and October 2010 were reviewed for the presence of 15demographic and clinical risk predictors, the majority of which were from a VA Readmission Risk Calculator. Additional variables specific to HF population were added to the risk calculator and included ejection fraction, substance abuse, and Black race. Binary and multiple logistic regression models were used to predict 30-day ACR. C-statistic was calculated to assess how good the model is in predicting who will be readmitted. Results. The patients studied were mostly male (98%), black (73.1%), and averaged 68 years old (SD 13.2). Of the 271 patients, 79 (29%) had at least 1 readmission; 8.1% had >1 readmission within 30 days of discharge. In bivariate logistic regression, patients with Creatinine > 2 were more than two times more likely to be readmitted (OR=2.35: 95% CI 1.32, 4.19). Patients with COPD had a similar likelihood of readmission (OR=2.36; 95% CI 1.25, 4.47), as did patients with renal failure (OR=2.41; 95% CI 1.25, 4.62). Black race, an added HF specific variable, had a significant influence on the likelihood of readmission (2.60; 95% CI 1.31, 5.16). In multivariate logistic regression with all of the predictors, only COPD (OR=2.70; 95% CI 1.32-5.52) and Black race (OR=2.07; 95% CI .97, 4.37) significantly predicted readmission. The C-statistic for the original model was .52, and improved only to .61 with the additional variables. Conclusion. The VA IPEC Readmission Risk Calculator derived in a medical-surgical population does not predict all-cause 30-day ACR after an index heart failure hospitalization. The addition of HF specific variables also did not improve the model. The study was limited by small sample size and use of a non-heart failure specific model. A future implication is that a heart-failure specific model with better C statistics could be tested and potentially be integrated into an electronic medical record so that an alert with an automated risk score could be developed and implemented. The impact of interventions based on risk assessment an open field of investigation.