Purpose To determine whether dynamic and personalized schedules of visual field (VF) testing and intraocular pressure (IOP) measurements result in an improvement in disease progression detection compared with fixed interval schedules for performing these tests when evaluating patients with open-angle glaucoma (OAG). Design Secondary analyses using longitudinal data from two randomized controlled trials. Participants 571 participants from Advanced Glaucoma Intervention Study (AGIS) and Collaborative Initial Glaucoma Treatment Study (CIGTS). Methods Perimetric and tonometric data were obtained for AGIS and CIGTS trial participants and used to parameterize and validate a Kalman filter model. The Kalman filter updates knowledge about each participant’s disease dynamics as additional VF tests and IOP measurements are obtained. After incorporating the most recent VF and IOP measurements, the model forecasts each participant’s disease dynamics into the future and characterizes the forecasting error. To determine personalized schedules for future VF tests and IOP measurements, we developed an algorithm by combining the Kalman filter for state estimation with the predictive power of logistic regression to identify OAG progression. The algorithm was compared against 1, 1.5, and 2 year fixed interval schedules of obtaining VF and IOP measurements. Main Outcome Measures Length of diagnostic delay in detecting OAG progression, efficiency of detecting progression, number of VF and IOP measurements needed to assess for progression. Results Participants were followed in the AGIS and CIGTS trials for a mean (standard deviation) of 6.5 (2.8) years. Our forecasting model achieved a 29% increased efficiency in identifying OAG progression (p<0.0001) and detected OAG progression 57% sooner (reduced diagnostic delay) (p= 0.02) than following a fixed yearly monitoring schedule, without increasing the number of VF tests and IOP measurements required. The model performed well on patients with mild and advanced disease. The model performed significantly more testing on patients who exhibited OAG progression than non-progressing patients (1.3 vs. 1.0 tests per year; p<0.0001). Conclusion Use of dynamic and personalized testing schedules can enhance the efficiency of OAG progression detection and reduce diagnostic delay as compared with yearly fixed monitoring intervals. If further validation studies confirm these findings, such algorithms may be able to greatly enhance OAG management.

OBJECTIVE: To assess the current pediatric nurse practitioner (PNP) workforce and to investigate the impact of potential policy changes to address forecasted shortages. METHODS:We modeled the admission of students into nursing bachelor's programs and followed them through advanced clinical programs. Prediction models were combined with optimal decision-making to determine best-case scenario admission levels. We computed 2 measures: (1) the absolute shortage and (2) the expected number of years until the PNP workforce will be able to fully satisfy PNP demand (ie, self-sufficiency).RESULTS: There is a forecasted shortage of PNPs in the workforce over the next 13 years. Under the best-case scenario, it would take at least 13 years for the workforce to fully satisfy demand. Our analysis of potential policy changes revealed that increasing the specialization rate for PNPs by 4% would decrease the number of years required until there are enough PNPs from 13 years to 5 years. Increasing the certification examination passing rate to 96% from the current average of 86.9% would lead to self-sufficiency in 11 years. In addition, increasing the annual growth rate of master's programs to 36% from the current maximum of 10.7% would result in self-sufficiency in 5 years.CONCLUSIONS: Current forecasts of demand for PNPs indicate that the current workforce will be incapable of satisfying the growing demand. Policy changes can result in a reduction in the expected shortage and potentially improve access to care for pediatric patients. WHAT'S KNOWN ON THIS SUBJECT:The number of nurse practitioner graduates in the United States has nearly doubled over the past 2 decades. However, the number of pediatric nurse practitioner (PNP) graduates has remained relatively flat, although the demand for PNPs is expected to increase. WHAT THIS STUDY ADDS:This study estimates the best-case shortage of PNPs over the next 25 years. We propose possible policy interventions to address key areas of the PNP workforce system and we compute their impact on the forecasted PNP shortage. Mr Schell drafted the initial manuscript, assisted in the collection of data, and analyzed and interpreted the results; Drs Lavieri and Toriello conceptualized and designed the study and reviewed and revised the manuscript; Mr Li assisted in the collection of data and analyzed and interpreted the results; Drs Martyn and Freed conceptualized and designed the study, assisted in the collection of data, and reviewed and revised the manuscript; and all authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work. 3 In particular, the competition between primary and subspecialty pediatric practices for the limited supply of PNPs will likely lead to difficulties in accessing pediatric subspecialty services. 4 A failure to address these pediatric health workforce concerns will affect pediatric care and will have long-term consequences on the health of the entire population. 5,6 It has been hypothesized that the increasing dema...

Background: Markov decision process (MDP) models are powerful tools. They enable the derivation of optimal treatment policies but may incur long computational times and generate decision rules that are challenging to interpret by physicians. Methods: In an effort to improve usability and interpretability, we examined whether Poisson regression can approximate optimal hypertension treatment policies derived by an MDP for maximizing a patient’s expected discounted quality-adjusted life years. Results: We found that our Poisson approximation to the optimal treatment policy matched the optimal policy in 99% of cases. This high accuracy translates to nearly identical health outcomes for patients. Furthermore, the Poisson approximation results in 104 additional quality-adjusted life years per 1000 patients compared to the Seventh Joint National Committee’s treatment guidelines for hypertension. The comparative health performance of the Poisson approximation was robust to the cardiovascular disease risk calculator used and calculator calibration error. Limitations: Our results are based on Markov chain modeling. Conclusions: Poisson model approximation for blood pressure treatment planning has high fidelity to optimal MDP treatment policies, which can improve usability and enhance transparency of more personalized treatment policies.