Defying Distance? The Provision of Medical Services in the Digital Age

This paper asks whether digital platforms can improve healthcare outcomes by enabling needs-based matching between patients and physicians unconstrained by geography. Amanda Dahlstrand studies digital primary care in Sweden during 2016-2018, exploiting nationwide conditional random assignment between approximately 200,000 patients and 143 doctors employed by Europe’s largest digital primary care provider. Patients who selected the “first available doctor” option (82% of first visits) were effectively randomized to a doctor within each 3-hour shift-by-date stratum, generating quasi-experimental variation free of the patient-doctor sorting that confounds identification in physical primary care.

The paper defines three observable dimensions of primary care physician skill: (1) identifying risky patients and triaging them to higher levels of care, measured by whether patients subsequently have an avoidable hospitalization within 90 days; (2) providing guideline-consistent treatment, measured by counter-guideline antibiotic prescriptions; and (3) leaving patients sufficiently informed so they do not unnecessarily seek additional in-person care within the following week. Doctor skill in each dimension is estimated via a value-added framework in a hold-out sample (Sample 1, the first 600 randomized consultations per doctor), using empirical Bayes shrinkage to reduce noise. Complementarities between doctor skill and patient risk are then estimated in a disjoint main sample (Sample 2).

A central finding is that doctor skill is task-specific rather than governed by a single latent ability: skills across the three tasks are not positively correlated, meaning doctors within general practice have individual “specializations.” A patient ranked in the top 1% of avoidable hospitalization risk who is matched to a doctor ranked in the top 10% at reducing avoidable hospitalizations experiences a 90% reduction in that adverse outcome, relative to a patient with the same risk profile matched to the worst-performing doctor. Patients not estimated as risky show effects indistinguishable from zero when matched to the same high-skilled doctors, establishing a strong complementarity between doctor type and patient risk.

Using the Average Match Function framework of Graham, Imbens, and Ridder (2014, 2020), the paper evaluates counterfactual reallocation policies. Reallocating only 2% of patients — those in the top 1% of predicted avoidable hospitalization risk — to doctors in the top 10% of triage skill reduces aggregate avoidable hospitalizations by 20% relative to random assignment, without adversely affecting counter-guideline prescriptions or other measured outcomes. Doctor skills across outcomes are not positively correlated, so this reallocation does not generate meaningful trade-offs. The paper benchmarks this matching policy against a selective hiring/expansion policy in which doctors with above-median skill in three tasks expand their hours by up to 70% at the expense of below-median peers; that policy yields no significant reduction in avoidable hospitalizations and only a 4% reduction in counter-guideline prescriptions — smaller gains than matching and harder to implement.

The paper also documents that physical primary care quality is worse in lower-income and more deprived areas of Sweden (a negative relationship between deprivation index and patient-reported experience is statistically significant at the 1% level in a cross-section of roughly 120-150 primary care centers in Region Skane). Because the estimated risk of avoidable hospitalization and prior avoidable hospitalizations are concentrated in the lower end of the income distribution, needs-based digital matching reallocates triage skill toward lower-income patients, severing the correlation between local area income and service quality. Simulating positive assortative matching on patient income and doctor skill — approximating existing healthcare inequalities — leads to more avoidable hospitalizations than random assignment, because the most vulnerable patients tend to be the poorest. Scope conditions: findings derive from a single digital primary care provider in Sweden, 2016-2018, pre-pandemic, covering conditions amenable to video consultation and a patient pool younger and somewhat more urban than the average Swedish citizen.

Q: What is the key identification strategy, and why is it valid in this setting but not in physical primary care? A: Patients who selected the “drop in” (first available doctor) option — 82% of first visits — were assigned to whichever certified doctor was next in the roster within a 3-hour shift-by-date stratum, a by-product of the first-come-first-served queue. Neither patients nor doctors could intervene in this digital process. The author validates the assumption by regressing doctor characteristics on patient characteristics controlling for shift-by-date fixed effects and finds characteristics are balanced. In physical primary care, endemic patient-doctor sorting means doctors do not meet a common support of patient types, preventing causal identification of doctor effects.

Q: How are doctor skill estimates constructed and why does the split-sample matter? A: Doctor skill in each task is estimated as an empirical Bayes-shrunk random effect from a value-added regression on Sample 1, each doctor’s first 600 randomized consultations (40% of the sample). Sample 2 (60%) is entirely disjoint and used to estimate complementarities between doctor skill and patient risk. The split-sample design prevents overfitting: doctor skill was estimated on different patients than those in Sample 2. The Durbin-Wu-Hausman test does not reject random effects (p = 0.16).

Q: What is the main quantitative result on avoidable hospitalization matching? A: A patient ranked in the top 1% of predicted avoidable hospitalization risk matched to a doctor ranked in the top 10% at reducing avoidable hospitalizations could reduce that patient’s avoidable hospitalizations by 90%, relative to the worst-performing doctor in that skill. At the aggregate level, reallocating only 2% of patients (those in the top 1% risk group) to high-triage-skill doctors reduces avoidable hospitalizations across the full patient population by 20% compared to random assignment.

Q: Does the avoidable hospitalization reallocation harm other outcomes? A: No. The paper explicitly evaluates the Average Reallocation Effect on counter-guideline prescriptions and additional in-person care seeking when optimizing for avoidable hospitalizations, and finds no significant adverse effects on these other outcomes. The author attributes this to the fact that doctor skills across tasks are not positively correlated, so reallocating triage-skilled doctors does not systematically remove skill from other dimensions.

Q: How does matching compare to selective hiring and hour expansion as a policy? A: Even expanding the working hours of doctors with above-median skill across three tasks by as much as 70% yields no significant reduction in avoidable hospitalizations and only a 4% reduction in counter-guideline prescriptions — both smaller gains than the matching policy. Matching outperforms hiring expansion because patients have heterogeneous needs that can be identified from prior healthcare records, and doctors have differentiated skill sets relevant to some patients but not others.

Q: What is the evidence that doctor skills are task-specific rather than reflecting a single latent ability? A: The estimated doctor effects across the three tasks — triaging to avoid hospitalizations, guideline-consistent antibiotic prescribing, and minimizing unnecessary follow-up care — are not positively correlated with one another. This means a doctor who is effective at one task is not systematically effective at others, indicating individual specializations within general practice that are not accounted for in standard primary care organization.

Q: How is patient risk for avoidable hospitalizations measured? A: A propensity score is estimated from pre-digital physical healthcare data (2013-2015), regressing past number of avoidable hospitalizations on demographic and healthcare utilization variables — including age, a disease index of chronic diagnoses, and previous hospitalizations — all variables already available in patient medical records. The top 1% of predicted risk scores are classified as “risky.” Patients in the risky group had on average 0.35 avoidable hospitalizations in the prior 3 years, versus 0.01 for non-risky patients.

Q: What is the distributional (equity) implication of needs-based matching versus income-assortative matching? A: Estimated risk of avoidable hospitalization and the count of prior avoidable hospitalizations are concentrated in the lower end of the income distribution. Needs-based matching therefore reallocates triage skill toward lower-income patients. Simulating positive assortative matching on patient income and doctor skill — approximating observed inequalities in physical care — produces more avoidable hospitalizations than random assignment, because the most vulnerable patients are often the poorest. Needs-based digital matching can sever the link between local area income and service quality.

Q: How does digital care usage sort by income and demographics in the data? A: At the extensive margin, the deprivation index (Care Need Index) is similar among digital users and non-users in Region Skane. However, at the intensive margin, individuals with a higher deprivation index who use the digital service have more appointments in it; similarly, lower-income users use the service more intensively. Digital care users are younger than non-users and are more likely to live in cities than the average Swedish citizen.

Q: What are avoidable hospitalizations and why are they the primary outcome? A: Avoidable hospitalizations (also called hospitalizations for ambulatory care sensitive conditions) are hospital admissions defined in the medical literature as preventable by adequate and timely primary care. They are coded using ICD-10 diagnosis codes listed in Page et al. (2007). The most common diagnoses in the 90-day post-consultation window are respiratory and genitourinary, conditions commonly treated in digital care. The outcome is rare (0.2% of patients in the sample), but high-stakes: an estimated 1.1 potential life years are lost per avoidable hospitalization, and in Sweden they cost an estimated SEK 7.1 billion (~$820 million) annually (7% of inpatient curative and rehabilitative care costs).

Q: What is the scope of the counter-guideline antibiotic prescription outcome? A: Non-adherence is coded against 16 guidelines from Sweden’s strategic programme against antibiotic resistance (Strama 2017, 2019), all designed to limit or narrow antibiotic use. The measured rate of non-adherence is described as quite low by international standards; the CDC estimates 28% of US antibiotic prescriptions are unnecessary, while the author’s sample rate is 2%. The guidelines require doctors to sometimes refuse patients who request antibiotics, introducing a behavioral compliance dimension to this skill.

Q: What are the costs and feasibility considerations for implementing needs-based digital matching? A: The paper characterizes matching as a “resource-neutral” policy because it reallocates existing doctors without hiring or training. The primary costs are a small increase in waiting time for some patients and the costs of importing data and developing the matching algorithm. Because the algorithm handles patient-doctor allocation while doctors retain all clinical decision-making, the policy functions as a complement to human skill rather than a substitute, which the author argues makes it less subject to “algorithm aversion.”

Q: Why does the paper restrict to each patient’s first digital consultation only? A: The first visit is the one subject to conditional random assignment; subsequent visits could reflect endogenous selection by patients who preferred a particular doctor or outcome. Using only first visits eliminates this concern. The restriction reduces the sample from approximately 378,000 to 210,171 patients (56% of the original), paired with 143 doctors who each had at least 600 randomized consultations.

Conditional random assignment: The allocation mechanism by which patients selecting the “first available doctor” option in digital primary care were assigned to whichever certified doctor was next in the shift roster, conditional on 3-hour shift-by-date strata — a by-product of the first-come-first-served queue rather than an intended experimental design.

Average Match Function (AMF): The conditional mean of a patient outcome given observable doctor type and patient type under random assignment, β(x,w) = E[Y|X=x, W=w], which serves as the building block for evaluating counterfactual reallocation policies.

Average Reallocation Effect (ARE): The difference in expected patient outcomes between a counterfactual doctor-patient assignment and the status quo random assignment, taking into account the externality on the patient from whom a high-skilled doctor is moved.

Task-specific doctor skill: The paper’s finding that primary care physician effectiveness is not governed by a single latent ability but varies across distinct tasks — triage/risk prediction, guideline-consistent prescribing, and minimizing unnecessary follow-up care — with skills across tasks not positively correlated.

Avoidable hospitalization: A hospital admission coded to a diagnosis (per Page et al. 2007 ICD-10 classification) defined in the medical literature as preventable by adequate and timely primary care, used as the primary high-stakes outcome measure (0.2% incidence in the sample within 90 days of a digital consultation).

Counter-guideline prescription: A prescription of an antibiotic in violation of one of 16 guidelines from Sweden’s Strama antibiotic resistance programme, all of which are designed to limit use or require narrower-spectrum first-line antibiotics; used as the primary guideline-adherence outcome (2% incidence in the sample).

Empirical Bayes shrinkage: A procedure applied to raw doctor value-added estimates in which the noisy estimate of doctor quality is multiplied by the ratio of signal variance to total (signal plus noise) variance, yielding a best linear predictor of the underlying doctor random effect and reducing noise from small-sample estimation.