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.

Our model improves the detection of concerning symptoms after radical cystectomy by optimizing the timing and number of outpatient encounters. By understanding how to design better outpatient followup care for patients treated with radical cystectomy we can help reduce the readmission burden for this population.

H ospital readmissions affect hundreds of thousands of patients every year, negatively impacting patients and placing a tremendous burden on the national healthcare system. Post-discharge checkup policies can reduce readmissions through early detection of health conditions, however, the methods behind designing effective checkup policies are poorly understood. Under current practice, up to 67% of readmitted patients return to the hospital before their first scheduled office visit. This work aims to develop effective checkup plans to monitor patients following hospital discharge, using a variety of checkup methods, including phone calls and office visits. We develop and analyze a new delay-time analysis model to identify the optimal type and timing of checkups to implement post-discharge monitoring plans. By analyzing the structure of optimal policies, we develop checkup schedules that can detect up to 43.7% more readmission-causing conditions experienced by readmission-bound patients. Further, we uncover simple rules of thumb that can help doctors design and improve monitoring plans even in the absence of advanced computer software or complex computations.

Analysts predict impending shortages in the health care workforce, yet wages for health care workers already account for over half of U.S. health expenditures. It is thus increasingly important to adequately plan to meet health workforce demand at reasonable cost. Using infinite linear programming methodology, we propose an infinite-horizon model for health workforce planning in a large health system for a single worker class; e.g., nurses. We give a series of common-sense conditions that any system of this kind should satisfy and use them to prove the optimality of a natural lookahead policy. We then use real-world data to examine how such policies perform in more complex systems; in particular, our experiments show that a natural extension of the lookahead policy performs well when incorporating stochastic demand growth.

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