Improving access to essential medicines via decision-aware machine learning

A critical challenge in healthcare systems in Low- and Middle-Income Countries (LMICs) is the efficient and equitable allocation of scarce resources, particularly essential medicines. This problem is complicated by limited high-quality data, which restricts the applicability of traditional data-driven techniques. We propose a novel decision-aware machine learning framework for essential medicines allocation, which additionally leverages multi-task learning to ensure sample efficiency and catalytic priors to ensure equitable allocation. In collaboration with the Sierra Leone national government, we performed a staggered, nationwide deployment of our system as a decision support tool and evaluated its impact using synthetic difference-in-differences. We find an estimated 19% increased consumption of allocated products in treated districts, demonstrating its efficacy at improving access to essential medicines. Our tool was subsequently scaled nationwide, covering an estimated 2 million wom..., , , # Data from: Improving access to essential medicines via decision-aware machine learning

[https://doi.org/10.5061/dryad.h9w0vt4tw](https://doi.org/10.5061/dryad.h9w0vt4tw)

## Description of the data and file structure

* Data S1: list of facilities
* Data S2: consumption data for evaluation

- Data S3: supply data (added random noises to comply data privacy agreement)

* Data S4: same as Data S2. Consumption data for evaluation but include control products

- Data S5: population based demand for each facility across products

### Files and variables

#### File: S1.csv

**Description:** facility list

##### Variables

* facility_type: categorizing facilities as Community Health Centre (CHC), Community Health Post (CHP), Maternal and Child Health Post (MCHP), or Clinic. 
* hf_pk: facility unique ID
* district: larger administrative regions, comprising a total of 16 districts

#### File: S2.csv.zip

**Description:** consumption data for evaluation. 

**Variables**

* **hf_pk**: unique fa..., ,