Neurosurgery-Led Digital Emergency Referral System in Khyber Pakhtunkhwa, Pakistan: Protocol for a Mixed Methods Implementation Study

BACKGROUND: Emergency referral systems in low- and middle-income countries (LMICs) are characterized by systemic inefficiencies, including prolonged transfer delays, interfacility miscommunication, and inadequate receiving-end preparation. In Khyber Pakhtunkhwa (KP), a province of Pakistan with a population exceeding 40 million, referral pathways remain predominantly paper-based and operationally fragmented. Tertiary medical institutions, including Lady Reading Hospital (LRH), Khyber Teaching Hospital (KTH), and Hayatabad Medical Complex (HMC), sustain a disproportionate burden of medically unnecessary referrals from peripheral facilities, culminating in institutional overcrowding, resource depletion, and attenuation of specialist neurosurgical and emergency care delivery. OBJECTIVE: This study aims to design, implement, and evaluate a neurosurgery-led digital emergency referral platform-the KP Medical Teaching Institution (MTI) Referral Application-across primary, secondary, and tertiary tiers of health care delivery in KP, with the primary intent of reducing unnecessary referral rates, abbreviating referral response intervals, optimizing inpatient bed utilization, and strengthening bidirectional inter-facility communication. METHODS: A convergent mixed methods design will be employed, incorporating a quantitative prospective cohort study alongside a qualitative exploratory component. The quantitative arm will adopt a census-based methodology, enumerating all emergency referrals processed through the digital platform over a 6-month pilot period commencing June 2026, with preimplementation historical referral data serving as the comparator. An estimated 5000-7000 referral episodes are anticipated. Primary outcomes include reduction in unnecessary referral rates and referral-to-response time. Secondary outcomes encompass inpatient bed occupancy rates, unanswered referral proportions, case acceptance and declination rates, remotely managed cases precluding physical transfer, patient mortality indices where ascertainable, and health care provider satisfaction scores. Statistical analysis will be performed using IBM SPSS, incorporating descriptive statistics, chi-square, or Fisher exact tests, paired comparisons, and multivariate logistic regression. The qualitative component will comprise structured surveys, semistructured interviews, and focus group discussions among purposively sampled health care professionals and patients, with thematic analysis conducted independently by 2 coders and findings integrated via a convergent mixed methods framework. RESULTS: System development, stakeholder engagement, user-centered iterative design, beta testing, and platform validation have been completed. Participant recruitment is pending pilot deployment in June 2026. Data collection is projected to conclude by November 2026, followed by analysis from December 2026 through February 2027. Peer-reviewed dissemination is anticipated in spring 2027. No external funding was received. CONCLUSIONS: This protocol outlines an integrated, multimodal assessment of a neurosurgery-based digital emergency referral system in a resource-constrained low- to middle-income country. In case the expected gains in referral efficiency and communication are realized, the results can be valuable evidence to justify the wider implementation of digital referral platforms in KP and other similar environments. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/90331.