The majority of decision analytic models submitted to health technology assessment (HTA) agencies are developed using Microsoft Excel. The approaches commonly used to construct these models have not been substantially updated in decades, and studies have found that spreadsheet models are often slower and more difficult to validate than models built using R. However, Excel and Google Sheets were recently upgraded to add support for dynamic array functions. This allows for many of the techniques used in R modeling to be applied to spreadsheet models. This paper provides a tutorial on how these new functions can be leveraged to build efficient Markov cohort models using modern spreadsheet software. A novel approach is presented for conducting Monte Carlo simulation using a single formula in one cell, without the need for Visual Basic for Applications (VBA) macros. A number of template formulas are also provided that can be used to assist in common modeling tasks, including constructing a Markov trace and calculating the table of probabilities needed to plot cost-effectiveness acceptability curves (CEACs). These template formulas may be directly copied and pasted into any spreadsheet model, with no add-ons, plug-ins, or additional packages required. These advancements have the potential to modernize how spreadsheet models are developed, simplifying their construction, improving their calculation speed, and reducing the time needed for validation. They will also aid in teaching more efficient approaches for decision analytic modeling to a new generation of students.