A medical prescription is a set of health care instructions that govern the plan of care for an individual patient, which may include orders for drug therapy, diet, clinical assessment, and laboratory testing. Clinicians have long used algorithmic thinking to describe and implement prescriptions but without the benefit of a formal programming language. Instead, medical algorithms are expressed using a natural language patois, flowcharts, or as structured data in an electronic medical record system. The lack of a prescription programming language inhibits expressiveness; results in prescriptions that are difficult to understand, hard to debug, and awkward to reuse; and increases the risk of fatal medical error. This article reports on the design and evaluation of Patient-Oriented Prescription Programming Language (POP-PL), a domain-specific programming language designed for expressing prescriptions. The language is based around the idea that programs and humans have complementary strengths that, when combined properly, can make for safer, more accurate performance of prescriptions. Use of POP-PL facilitates automation of certain low-level vigilance tasks, freeing up human cognition for abstract thinking, compassion, and human communication. We implemented this language and evaluated its design attempting to write prescriptions in the new language and evaluated its usability by assessing whether clinicians can understand and modify prescriptions written in the language. We found that some medical prescriptions can be expressed in a formal domain-specific programming language, and we determined that medical professionals can understand and correctly modify programs written in POP-PL. We also discuss opportunities for refining and further developing POP-PL.

Abstract Enumerations represented as bijections between the natural numbers and elements of some given type have recently garnered interest in property-based testing because of their efficiency and flexibility. There are, however, many ways of defining these bijections, some of which are better than others. This paper offers a new property of enumeration combinators called fairness that identifies enumeration combinators that are better suited to property-based testing. Intuitively, the result of a fair combinator indexes into its argument enumerations equally when constructing its result. For example, extracting the n th element from our enumeration of three-tuples indexes about $\sqrt[3]{n}$ elements into each of its components instead of, say, indexing $\sqrt[2]{n}$ into one and $\sqrt[4]{n}$ into the other two, as you would if a three-tuple were built out of nested pairs. Similarly, extracting the n th element from our enumeration of a three-way union returns an element that is $\frac{n}{3}$ into one of the argument enumerators. The paper presents a semantics of enumeration combinators, a theory of fairness, proofs establishing fairness of our new combinators and that some combinations of fair combinators are not fair. We also report on an evaluation of fairness for the purpose of finding bugs in programming-language models. We show that fair enumeration combinators have complementary strengths to an existing, well-tuned ad hoc random generator (better on short time scales and worse on long time scales) and that using unfair combinators is worse across the board.

Medical professionals have long used algorithmic thinking to describe and implement health care processes without the benefit of the conceptual framework provided by a programming language. Instead, medical algorithms are expressed using English, flowcharts, or data tables. This results in prescriptions that are difficult to understand, hard to debug, and awkward to reuse. This paper reports on the design and evaluation of a domain-specific programming language, POP-PL for expressing medical algorithms. The design draws on the experience of researchers in two disciplines, programming languages and medicine. The language is based around the idea that programs and humans have complementary strengths, that when combined can make for safer, more accurate performance of prescriptions. We implemented a prototype of our language and evaluated its design by writing prescriptions in the new language and administering a usability survey to medical professionals. This formative evaluation suggests that medical prescriptions can be conveyed by a programming language's mode of expression and provides useful information for refining the language. Analysis of the survey results suggests that medical professionals can understand and correctly modify programs in POP-PL.