Context: Partner services for HIV and sexually transmitted diseases, a public health intervention activity recommended by the Centers for Disease Control and Prevention, includes counseling, partner notification, linkage to care, and referral to other services. Objective: A time study of partner services case investigations documented differences in times to process HIV/sexually transmitted disease cases. Setting: Cases were from 9 local and regional sites in New York. Participants: Fifty-two partner services disease investigators documented 542 randomly selected cases (271 chlamydial infections, 162 gonorrhea, 48 HIV, and 61 syphilis cases) assigned between June and September 2014. Cases were the unit of the analysis and represented 6.9% of all partner services investigations in 2014. Design: Cases were selected via stratified random sampling of infections assigned to staff. For each case, disease investigators completed a standard time study form to document the time spent on specific tasks and other outcomes. Kruskal-Wallis tests for continuous variables and chi(2) tests for categorical variables assessed variation in outcomes across infection type. Main Outcome Measures: Outcomes included minutes spent on specific tasks (such as medical provider and index case outreach, travel, and partner notification), days the case remained open, disposition codes, and number of partners reached. Results: Case processing times varied, with HIV and syphilis tasks taking more minutes (P < .001) and cases staying open for more days (P < .001). Partners were notified in 33% of cases overall, with more notifications in syphilis (44%). Most time (median = 77%) was spent on index cases and 2% (median) on partner notification, with a wide range across cases. Conclusions: Given their chronic resource constraints, public health agencies must identify efficient methods to allocate resources, including which infections to prioritize. Documenting how workers allocate time across cases is essential to improving the effectiveness and efficiency of this program and generating the data to model return on investment.