Evaluating the Rollout of Estonia's Enhanced Care Management Program
Conditions: Multi-morbidity, Non-communicable Diseases
Sponsor: Harvard School of Public Health (HSPH)

 
 
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Evaluation of Estonia’s Enhanced Care Management 
Scheme: Protocol for a cluster randomized trial 
 
  Benjamin Daniels1*, Daniel Rogger3, Meyhar Mohammed3, Katre Väärsi3  and Kevin Croke2 
 
 
1 Georgetown University, Washington, DC, USA. Email: bd581@georgetown.edu  
2  Department of Global Health and Population, Harvard TH Chan School of Public Health, USA. Email: 
kcroke@hsph.harvard.edu  
3 Development Impact Evaluation, World Bank, USA. Emails: drogger@worldbank.org, mlnu15@worldbank.org, 
katrevaarsi@gmail.com.  
 
*Correspondence to: Benjamin Daniels, Georgetown University email: bd581@georgetown.edu 
Date:  April 1, 2023 
NCT number:  to be determined 
Keywords 
Non-communicable diseases, health systems, aging, chronic illness, electronic health records, primary care quality, 
randomized control trial 
 
 

 
 
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ABSTRACT 
 
Background: Estonia’s aging population faces an increasing burden of non-communicable 
diseases (NCDs) and a growing population suffers with multiple chronic conditions. The 
Estonia Health Insurance Fund (EHIF) seeks to support primary care physicians to improve 
care for complex patients with multiple chronic conditions. The Enhanced Care Management 
(ECM) program entails training family physicians to identify complex patients, co-develop 
proactive care plans with them, and to undertake more active outreach to and management of 
these patients.  
Methods: In this protocol we describe a randomized controlled trial in partnership with 
EHIF to evaluate the impact of ECM training for physicians. The study team enrolled a 
randomly selected 97 family physicians out of the 786 family physicians practicing in Estonia. 
Among those physicians’ 6,739 ECM-eligible patients, 2,389 patients were randomly selected 
for enrolment into the ECM program. Using administrative records, we will evaluate the 
effects of ECM enrolment on: (1) health care utilization; (2) provider management of tracer 
conditions; and (3) markers of quality of care, such as hospital admission for primary health 
care-sensitive conditions.  

 
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1 BACKGROUND 
As the world makes progress on reducing infectious diseases and other drivers of premature mortality, non-communicable 
diseases (NCDs) such as diabetes, hypertension, and cardiovascular diseases have come to account for over 70% of deaths 
worldwide1. High and middle income countries in particular have faced rapidly rising burdens of NCDs, as improving social 
conditions and advanced medical treatments enable their populations to survive into old age. In these populations, co-occurrence 
of multiple chronic illness, also known as multi-morbidity, is also growing. These shifts in population health imply major new 
demands on the health system, as patients with multiple chronic conditions typically require more care, from multiple levels of 
the health system, over extended periodsof time. One response to this challenge in the US and Europe has been a move towards 
more proactive and comprehensive primary care models for complex patients. These models include more proactive care by 
primary care providers and their team, and greater efforts to coordinate care for patients based on their specific needs for 
specialist care, nursing care, preventive care, improved coordination around care transitions, and greater attention to social and 
economic needs of patients. 
Estonian policymakers have developed a model to improve primary care, known as Enhanced Care Management (ECM). 
ECM program components include physician coaching, risk stratification, co-creation of care plans, and proactive follow up 
with patients between visits and after hospitalizations, and coordination with social services when applicable for patient. ECM’s 
theory of change is that through sustained coaching, physicians can provide better care by following this model. With better 
management of chronic care conditions, patients will increase their appropriate use of primary health care services, and will have 
reduced need for secondary and tertiary services for ambulatory-care sensitive conditions. Over time, this improved care should 
result in improved health and quality of life for patients, and increased patient satisfaction with care. 
This protocol presents a pre-analysis plan for the evaluation of the Estonian Enhanced Care Management program.  
 
Setting 
Estonia’s health system faces growing challenges with population aging and growing burden of NCDs. As of 2017, 
approximately 50% of the population was above the age of 44 and 50% of the total population had at least one chronic illness. 
Among those aged 0-18, 18.2% had at least one chronic illness and 3.4% had multiple chronic illnesses, while for those over 45, 
65.6% had at least one chronic illness and 71% had multiple chronic illness. Notably, mortality rates for cardiovascular and 
circulatory system diseases are much higher than in comparator EU countries, and Estonia has among the higher rates of avoidable 
hospital admissions in the EU2. 
 
ECM Intervention 
The Enhanced Care Management (ECM) intervention consists of training and coaching family physicians and their teams to 
develop holistic care and pro-active outreach plans for chronically ill patients or those vulnerable to developing chronic illnesses. 
The core goal of ECM is to improve the quality of care provided to complex patients, including by increasing the use of preventive 
care, improving coordination of care across health system levels, and increasing patient involvement in care. These elements can 
improve patient health and quality of life, and may reduce the need for curative medical services—for example, by supporting 
patients with type 2 diabetes to improve their diet and increase physical activity to limit further deterioration in their health. 
ECM practices include improved tracking of tests and referrals, follow-up by PHC providers after hospital discharges, 
tracking of medication adherence, monitoring of patients between clinic visits, and greater focus on clinical quality. It includes 

 
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four elements: identifying high-risk patients through risk stratification, developing care management plans by the primary care 
physician in consultation with the patient, proactively linking care providers together, and developing a team approach between 
patients and their caregivers.  
 
2. METHOD AND DESIGN 
2.1 ECM IE Design Overview 
This study incorporates a two stage randomization, first at the clinic level and second at the patient level.  
 
2.2 Clinic randomization 
EHIF identified 421 clinics (786 providers) who were eligible for the ECM program. The study team then excluded clinics which 
had participated in the pilot study, those not currently operational, as well as clinics with five or more practicing providers. From 
the remaining 546 clinics, we conducted a stratified sampling via coarsened exact matching with two quality of care measurement 
indicators – the practice’s Quality Bonus Scheme (QBS) score and a management score given to each clinic3. We grouped all the 
clinics into blocks of similar performance based on the QBS and management score. From each performance block, we randomly 
selected one-fourth of the clinics into ECM program.  
This sampling resulted in 144 providers randomly selected into ECM and 402 providers in the control group. All providers in 
randomly selected clinics were enrolled in ECM. After the clinic randomization and after providers were invited to join the ECM 
program, 47 providers have refused or dropped out. The most common reasons given were lack of time, or other logistical issues, or 
in some cases health problems with several providers themselves. Since these providers dropped out of the program before the 
patient classification began, their patient population is excluded from randomization into the program.. 

 
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2.3 Patient randomization 
The next stage of randomization was conducted at the patient level. From each participating ECM provider, 25 patients were 
eligible for selection into the ECM program. (This number was based on EHIF’s budgetary limitations for the program). Each 
year, EHIF’s algorithm uses their Mini Information System Portal (MISP) to update the list of at-risk patients who have multiple 
chronic illnesses. For this project, the providers selected in the ECM group evaluated these patients and assigned an additional 
risk score to each of the patients identified in MISP, as follows: 
• 1-Mild/moderate risk of deteriorating health 
 
• 2-Severe risk of deteriorating health 
 
Given the mix of mild/moderate and severe patients within each provider, we conduct a stratified random sampling of patients 
into ECM based on the risk classification, such that every patient within each risk classification group has equal probability of 
selection, and there are at most 25 patients selected into the ECM program from each provider. All the patients within every 
provider are stratified into two strata – one for mild/moderate risk and second for severe risk. From each strata we then randomly 
selected patients into the treatment proportional to their share in the strata with a maximum of 25 patients selected in treatment. 
Five providers had identified fewer than 25 patients who had a risk of deteriorating health. For these providers, all the patients 
were assigned to treatment. The patients assigned to ECM were invited to join ECM by their providers; all patient acceptances 
and refusals are recorded in their electronic health records which will be shared with the research team during the analysis phase. 
 
2.4 Data 
2.4.1 Provider Sample 
EHIF has a network of approximately 800 primary care providers (family physicians), roughly 70% of whom work in a solo 
practice clinic4. The research team was provided with a dataset of all the clinics, linked providers, with their annual QBS score. 
This was the basis for construction the sampling frame for the provider randomization. 
In order to construct performance blocks for randomization, we used the QBS data and management scores for 2019. QBS 
is Estonia’s performance-based incentive program. We constructed a need- adjusted QBS score re-weighting each indicator based 
on the experience of the scheme, awarding proportional credit to providers at an indicator level and adjusting the coverage rates 
for providers based on the patient need5. The management score is a sum of points awarded on 15 indicators about the clinic’s 
working and managerial practices. The average score per clinic on management indicators is 10 and the average need-adjusted 
QBS score per clinic is 306. Because the management score was only available at the clinic level, we average the QBS score for the 
clinic for sampling. 

 
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2.4.2 Patient Sample 
EHIF’s Mini Information System Portal (MISP) is the portal used by EHIF to list patients who have multiple chronic illnesses, 
and contains information such as the patient’s family physician, and the number of co-morbidities. We matched this dataset to the 
list of ECM providers, to generate lists of higher-risk patients. Additionally, every ECM provider gave an additional risk score 
(mild/moderate or severe risk) to each of the patients in MISP. 
 
 
2.1.1 EHIF billing data 
For all the data on the outcomes such as health care utilization, provider management of tracer conditions and markers of quality 
of care we use longitudinal digitized billing records. This data includes every health system interaction for Estonian citizens covered 
by EHIF. We have electronic billing records spread over eight health care services categories - primary health care, day care, 
outpatient care, outpatient nursing care, outpatient rehabilitation care, inpatient care, inpatient nursing care, and inpatient 
rehabilitation care over a 10 year period (2009 until 2019). Each dataset contains three elements. First, every care type contains a 
claims summary dataset, identified by a bill number. These are initiated by the provider after every episode of care. This data 
includes the duration of a treatment, type of care, and physician and patient details in reference to the care episode. Second, 
claims summary dataset is accompanied by a diagnosis dataset that describes all the diagnosis which were relevant to the given 
care episode. Finally, the third accompanying dataset is the procedures dataset that describes all the medical procedures that were 
conducted within a given episode of care. We have the billing record containing the associated single insurance payer insurance 
fund claims, the diagnosis and treatment for each claim filed. In each of these claims we use the International Classification of 
Disease (ICD) codes of diagnoses, and the procedures. 
All the key outcomes of this study will utilize these systems of data, de-identified in compliance with Health Insurance Portability 
and Accountability Act (HIPAA) regulations. 
2.2 Hypotheses 
ECM’s theory of change is that the program’s coaching will enable family physicians and their staff to deliver better care to 
ECM patients. Patients will increase the use of primary healthcare services, and the care that they receive will be of higher quality. 
With better management of chronic conditions, patients will have less need for secondary and tertiary services such as (avoidable) 
inpatient hospital admissions and re-admissions, and ambulatory specialist services, and they will experience better health and 
higher quality of life. 
 
2.3 Outcomes 
Given these hypotheses, we focus on three outcome domains: overall healthcare utilization, provider management of tracer 
conditions, and measures of PHC-sensitive acute care. In domains one and three, we highlight 3 primary outcomes below; the 
remaining indicators are all secondary study outcomes. 
• 1. Healthcare utilization: This outcome will be continuous and measures the following indicators during the intervention 
year, at the patient level: 
 
– number of primary health care interactions (primary outcome) 
 

 
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– number of inpatient care interactions (hospitalizations) (primary outcome) 
 
– number of outpatient (ambulatory) services (primary outcome) 
 
– number of inpatient post-hospitalization services (nursing and rehabilitation) 
 
– number of outpatient post-visit services (nursing and rehabilitation) 
 
– number of follow up by telephone 
 
– number of follow ups due to chronic illness 
 
• 2. Provider management of tracer conditions: This outcome measures provider compliance with Domain II QBS standards as 
the measure of quality care provision. The QBS has a set of guidelines for monitoring outlined for managing type 2 
diabetes (ICD10 E11), hypertension (ICD10 I12-115) and myocardial infarction (ICD10 I21-I23, I25.2). This outcome 
measures the share of patients who are managed in compliance with the guidelines for monitoring, out of the total 
number of patients who have a diagnosis of the three conditions in the intervention year aggregated at the provider level. 
The guidelines for each condition are described in Table 1. 
Example outcomes for this domain include: 
 
– For type-2 diabetes: monitoring of glycosylated Hb (HbA1C), creatinine, cholesterol level (1 per year). 
– For hypertension high risk patients: monitoring of cholesterol level, cholesterol fractions, glucose/glycosylated Hb, creatinine 
(1 per year); counselling and appointment with family nurse (per year). 
– For myocardial infarction patients: Monitoring of cholesterol level (1 per year), glycosylated Hb (HbA1C) (1 per 
year), counselling with family nurse (1 per year). 
 
 

 
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  Table 1. QBS Compliance Guidelines 
Category 
Indicator 
Description 
Measurement 
Diabetes - type II 
Monitoring 
Glycosylated hemoglobin 
1 per year 
Creatinine values 
Cholesterol values 
Cholesterol fraction values 
1 per 3 years 
Counselling for chronic patient 
1 per year 
Diabetes - type II 
Medication 
Prescribed for all diabetes type-II patients 
6 prescriptions in 14 
months 
Hypertension I (low risk) 
Monitoring 
Glucose or glycosylated hemoglobin 
1 per 3 years 
Cholesterol 
Counselling for chronic patient 
1 per year 
Appointment by family nurse 
Hypertension II (moderate risk) 
Monitoring 
Cholesterol determined for patients under 
80 years of age 
1 per year 
Cholesterol fractions determined for 
patients under 80 years of age 
Glucose or glycosylated hemoglobin 
Creatinine 
ECG 
1 per 3 years 
Counselling for chronic patient 
1 per year 
Appointment by family nurse 
Hypertension III (high risk) 
Monitoring 
Cholesterol determined for patients under 
80 years of age 
1 per year 
Cholesterol fractions determined for 
patients under 80 years of age 
Glucose or glycosylated hemoglobin 
Creatinine 
Counselling for chronic patient 
Appointment by family nurse 
Hypertension medication 1 
Medication 
Percentage of active ingredients based 
prescriptions for hypertension patients (all 
risk levels) 
1 per year 
Hypertension medication 2 
Medication 
Prescriptions for moderate or high-risk 
hypertension patients 
6 prescriptions in 14 
months 
Myocardial Infarction (MI) 
Monitoring 
Cholesterol 
1 per year 
Glucose or glycosylated hemoglobin 
Cholesterol fractions 
Counselling for chronic patient 
Myocardial infarction (MI) 
Medication 
Prescription of beta-blockers treatment 
group (incl combination drugs) 
6 prescriptions in 14 
months 
Prescription of statins treatment group (incl 
combination drugs) 
6 prescriptions in 14 
months 
Hypothyroidism 
Monitoring 
TSH (thyroid stimulating hormone) 
determined 
1 per year 
Total 
 
 

 
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• 3 PHC-sensitive acute care: We will monitor a set of selected outcomes which capture quality of primary care, following 
indicators created by the Organization for Economic Co-operation and Development (OECD). Based on the OECD 
framework, we will construct the following outcomes: 
 
– Avoidable hospital admissions for asthma, COPD, diabetes, congestive heart failure, and hypertension, defined as the 
number of hospital admissions with any of the above as primary diagnosis (Primary outcome) 
– Emergency department visits (for any condition) (Primary outcome) 
 
– Inpatient readmission within 90 days after any previous inpatient admission (Primary outcome) 
 
– Inadequate follow up care for patients hospitalized for acute inpatient care or surgery: cardiovascular disease, acute 
myocardial infarction, stroke, hip fracture, cholecystectomy. (This measure is defined as the rate of patients who have 
follow up from family physician within 90 days of discharge)  
– Incomplete discharge from acute in-patient care (for heart failure, acute myocardial infarction, unstable angina) 
– Share of prescriptions purchased out of all the prescribed medications by provider  
 
– Incidence of new diagnosis of tracer conditions 
 
2.4 Statistical model 
For outcome (1) and (3), we specify the difference in means in the intervention year using the below specification: 
𝑌!"# =  a + 𝛽(𝐸𝐶𝑀!  × 𝑃𝑜𝑠𝑡#) + 𝛾$𝐸𝐶𝑀! + 𝛾%𝑃𝑜𝑠𝑡# + 𝑃! + 𝜏# + 𝜋" + (𝜖! + 𝜂" + 𝜁#) 
Here, 𝑌!"# is the outcome of patient i, with ECM provider k, at time t. 𝐸𝐶𝑀! is an indicator that the patient is selected into the 
ECM treatment and 𝑃𝑜𝑠𝑡# is an indicator that the ECM treatment period has started. Therefore 𝛽 is the difference-in-difference 
coefficient of interest. 𝑃! are a vector of patient-level controls, such as prior health care seeking, risk status, or health effects (in 
various models). 𝜏# are year fixed effects and 𝜋" are provider controls or fixed effects. Standard errors are clustered with 
provider-year random effects indicated as 𝜂" + 𝜁#. 
 
For outcome (2), we specify the difference in means in the intervention year using the below specification: 
𝑌"# =  a + 𝛽(𝐸𝐶𝑀"  × 𝑃𝑜𝑠𝑡#) + 𝛾$𝐸𝐶𝑀" + 𝛾%𝑃𝑜𝑠𝑡# + 𝜏# + 𝜋" + (𝜂" + 𝜁#) 
Here, 𝑌"# is the outcome provider k, at time t. 𝐸𝐶𝑀! is an indicator that the provider is selected into the ECM treatment and 𝑃𝑜𝑠𝑡# 
is an indicator that the ECM treatment period has started. Therefore 𝛽 is the difference-in-difference coefficient of interest. 𝜏# 
are year fixed effects and 𝜋" are provider controls or fixed effects. Standard errors are clustered with provider-year random 
effects indicated as 𝜂" + 𝜁#. 
 
 
 
 
Funding statement 

 
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This project received funding from the Georgetown University McCourt School of Public Policy Massive Data Institute, the 
World Bank’s Research Support Board, and the World Bank Strategic Impact Evaluation Fund. 
 
Ethics 
This study was approved by Harvard Longwood IRB protocol IRB20-114. 
 

 
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References 
1. World Health Organization, Noncommunicable diseases: Progress monitor 2020. (2020). 
 
2. 
World Bank. The state of health care integration in Estonia (2015). 
 
3. 
Blackwell, M., Iacus, S., King, G., et al. CEM: Coarsened exact matching in Stata. The Stata J. 9, 524–546 (2009). 
4. 
Kurowski, C., Chandra, A., Finkel, E., et al. The state of health care integration in Estonia: Summary report. (2015). 
5. Croke, K., Daniels, B., Rogger, D., et al. Improving incentives for fairness in QBS: A “need-adjusted” approach to 
coverage. (2019).