8. Medical Diagnostic Centres, Poland

The number of people living with one or multiple chronic conditions has been rising. Primary care, as the first point of contact with the healthcare system, plays an important role in preventing, managing and controlling the progression of chronic diseases. Despite widespread acceptance that a high functioning primary care system is essential for improving health outcomes and containing costs, international research shows people with chronic diseases frequently do not receive necessary preventative care, further, the care they do receive is not co-ordinated (OECD, 2020[]).

Relative to other OECD and EU Member States, Poland has a weak primary care system. A strong primary care system can reduce or eliminate hospitalisations for diseases such as diabetes and congestive heart failure (CHF). Therefore, hospitalisations for these diseases measure the strength of a country’s primary care system. Poland, in 2016, recorded 511 discharges for CHF per 100 000 people, which was the second highest of any EU Member State (OECD, 2019[]).

In response to these challenges, Poland implemented a Primary Healthcare (PHC) Plus programme covering over 40 primary care facilities, each of which offers integrated, patient-centred care. One of these primary care facilities is the Medical and Diagnostic Centre (MDC), established in 2015 in the region of Siedlce. An overview of the MDC model of care is outlined below:

  • Preliminary visit and diagnostic tests: the patient has an initial visit to the doctor who prescribes a list of tests relevant for the patient. The patient has these tests performed outside the preliminary test.

  • Main complex visit: once the tests results are available, the patient attends a follow-up appointment with the same doctor and receives a comprehensive assessment. The assessment includes a physical examination by a nurse (e.g. measurement of BMI and blood pressure) followed by a discussion with the doctor who goes over results from the diagnostic tests. Based on test results, the physical examination, medical history, and patient needs, the doctor classifies the patient into one of five risk groups (see Box 8.1) and develops an “Individual Medical Care Plan” (IMCP). The IMCP outlines treatment plans and recommended follow-up appointments. The IMCP is available to the patient’s therapeutic team, which includes a GP, psychiatrist, psychologist, dietitian, occupational therapist and physical therapist. The therapeutic team also have access to patient data via the integrated electronic health record (EHR).

  • Meeting with the care co-ordinator: immediately following the main complex visit, the patient visits their care co-ordinator who is responsible for co-ordinating treatment and, in agreement with the patient, sets up the necessary appointments, including an educational session. The main complex visit and the meeting with the care co-ordinator takes approximately 90 minutes.

  • Education sessions: MDC developed disease-specific education programs to help patients self-manage, which are run by nurses, nutritionists and dieticians.

  • Follow-up visits: the IMCP indicates the number of follow-up visits the patient requires, which is based on their risk group. For example, a patient in risk Group 3 (stable but requires periodic check-ups) is assigned two follow-up visits a year compared to Group 4 (unstable patient) who requires 3-4 follow-up visits (see Box 8.1).

This section analyses MDC against the five criteria within OECD’s Best Practice Identification Framework – Effectiveness, Efficiency, Equity, Evidence-base and Extent of coverage (see Box 8.2 for a high-level assessment of MDC). Further details on the OECD Framework are Annex A.

Results for effectiveness are provided for MDC and the PHC Plus pilot. The latter includes findings from an evaluation of 41 pilot sites, which includes MDC. The results are considered relevant for this cases study given all pilot sites are based on the same model of care.1

The objective of MDC is to improve patient experiences, health outcomes, and reduce utilisation of secondary care services and costs. This section presents data measuring the impact of MDC on healthcare utilisation using data over the period 1 April 2014 to 31 December 2020. Results from the analysis provide an indication, but do not confirm, MDC’s impact on utilisation given limitations in the study design (explored further under the “Evidence-base” criterion).

The average number of patient visits did not change markedly despite an increase in the average patient age. Between 2014-20, the average age of patients enrolled in MDC increased from 48.25 to 51.97. Over the same period, the average number of GP (Figure 8.1) and specialists visits fell, in particular for gynaecological appointments2 (Figure 8.2). Given morbidity and thus healthcare utilisation increase with age, the results indicate, but do not conclude, MDC improved patient outcomes.

In 2020, the World Bank released findings from its evaluation of PHC Plus two years after the implementation of the pilot programme. The evaluation found an improvement in patient reported experiences and outcomes, and health literacy, but no decrease in the utilisation of hospital services.

Patients recorded a better care experience. Patients enrolled in PHC Plus recorded higher levels of patient reported experience measures (PREMs) compared to patients who receive care in a GP single practice (control group). For example, the PREM score reflecting whether the patient felt the specialist had knowledge of their medical history is over eight points higher for those enrolled in PHC Plus (53.6 versus 45.3 out of a possible 100 points) (Figure 8.3). Patients enrolled in PHC Plus also reported better integration of healthcare, albeit marginally (World Bank, 2020[]).

Patients reported better outcome measures. PHC Plus patients with diabetes and asthma/COPD reported lower disease severity. Conversely, disease severity for patients with back pain was similar between the two groups (Figure 8.4).

The number of hospitalisations increased despite faster access to care. The average monthly number of one-day hospital stays increased by 8.86 days while longer hospital stays (i.e. +1 day) increased by 12.59 days after the introduction of PHC Plus. These results may reflect an ongoing disconnect between primary and secondary care in Poland. The same study showed initial results indicating “potentially faster” access to tests and healthcare. For example, on average, a patient with diabetes who is enrolled in PHC Plus receives a comprehensive assessment within 68.52 days, which is markedly lower than the 78 days the same patient would wait to see an outpatient centre specialising in diabetology (World Bank, 2020[]).

Patients report being more health literate and less reliant on others for assistance. PHC Plus patients recorded a health literacy score of 79.19 points (out of 100) compared to 76.35 for the control group. Fourteen percent of PHC Plus patients reported involving their family compared to 28% in the control group (World Bank, 2020[]).

Efficiency data from MDC or the PHC Plus intervention are not available. Instead, this section summarises findings from key sources of literature, which conclude that high performing primary healthcare systems help contain spending – see Box 8.3 (OECD, 2020[]).

More than half of all MDC patients are located outside urban areas. Equity of access has been analysed, at a high-level, using patient data across Degurba (degree of urbanisation) classifications (1 = densely population, 2 = intermediate levels of population, and 3 = thinly populated). Results from the data show more than half of all MDC patients are located outside densely populated areas (61% of all patients or 79 136 patients in total) (see Figure 8.5). These results indicate MDC successfully reaches geographically disadvantaged patients.

MDC actively reaches out to patients living in rural areas. People living outside major urban areas have lower access to healthcare services. As part of the MDC intervention, specialist physicians must travel to small health centres located in rural areas on specific days, as a way to address geographic inequalities.

MDC participates in complementary activities designed to reduce social health disparities. In addition to its core services (see “Intervention description”), MDC supports programs that actively reach out to the community, including disadvantaged groups. For example:

  • “Healthy Community” contest: the Polish Union of Oncology runs a campaign promoting screening tests, which are performed by the MDC. The contest contributed to an increase in the number of people screened, however, due to data constraints, it is unclear if these people are from priority population groups who would otherwise not have been screened.

  • Patient transport services: in addition to its core services, MDC runs a preventative care campaign, which offers patients screening and diagnostic tests at their local MDC (usually every Saturday). Free transportation is available for people who may have difficulty accessing their local MDC.

This section analyses the quality of evidence to evaluate the effectiveness of MDC and PHC Plus (Table 8.1). In summary:

  • The impact of MDC on healthcare utilisation was evaluated using data from all patients (n = 61 776 to 84 677 depending on the year) over a period of six years (2014-20). Data from patients showed that despite an increase in the average patient age, utilisation of GP and specialists services per person stayed the same or declined (see “Effectiveness”). These results only provide an indication that MDC reduces healthcare utilisation given the study design. For example, the results do not include data for a control group, which is necessary to ascertain if trends in utilisation are a result of MDC, further, the analysis does not control for potential confounders (e.g. socio-economic status). Nevertheless, data from MDC patients was collected using routine utilisation data, which is both valid and reliable.

  • The World Bank evaluated PHC Plus using cross-sectional survey data (2019-20) from an intervention and control group. In total, patients from 38 PHC Plus sites were included in the intervention group and 63 primary care facilities in the control group. Differences between the intervention and control group prior to the analyses were not reported, however, the methodology controlled for key confounders including age, gender, facility size, education and self-perceived financial status. Data to measures patient reported experiences and outcomes were collected using valid and reliable tools.

Between 2014 and 2020 the number of MDC patients grew by 37% – i.e. from 61 776 to 84 677 (Figure 8.6). As of 2020, 22% of patients were aged 0-17, 52% between ages 18 to 59 while the remaining 26% are at least 60 years of age (Figure 8.7).

Policy options to enhance the overall performance of MDC are summarised below. Many of the options target higher-level policy makers (e.g. at the national level as opposed to MDC administrators), given they involve significant structural change.

Continue effort to promote the use of electronic health records (EHRs) in primary care. In 2011, Poland introduced the Act on Information System in Healthcare requiring all patient information to be uploaded electronically by 2014, which was subsequently delayed to 2017 (Czerw et al., 2016[]). As of July 2021, it is compulsory to record medical events using EHRs (specifically to the P1 Platform). Given the importance of EHRs in supporting integrated, patient-centred care, policy makers should continue to promote efforts to safely and securely share patient information electronically.

Improve communication between primary care professionals and patients by continuing to improve Poland’s health portal. High-quality primary care models encourage patients to play an active role in improving their health (e.g. shared-decision making, support for self-management). Digital tools, such as health portals (often referred to as patient-provider portals), play a key role in this context. In 2018, Poland introduced the Patient’s Internet Account (IKP). Through the IKP patients can obtain information on their e-prescriptions, e-referrals, as well as for their children, sick leave, and a history of visits and medication together with dosage amounts. Patients can also schedule a COVID-19 vaccination appointment as well as download an electronic Digital COVID-19 Certificate (EU DCC) confirming that the vaccination has been administered. Policy makers should continue efforts to expand the services and capabilities of the IKP.

Build towards using data-driven means to stratify patients into risk groups. As part of the MDC intervention, doctors allocate patients into risk groups (see Box 8.1) based on information collected during the main complex visit (e.g. a physical examination, diagnostic tests). Risk stratification is commonly employed amongst OECD countries, however, increasingly countries rely on big data to stratify patients. For example, the Catalan Open Innovation Healthcare Hub have developed an adjusted morbidity grouper (GMA) algorithm, which uses data from EHRs3 to stratify patients into risk groups. Sophisticated digital methods improve the accuracy and efficiency of population risk stratification.

No specific policy options to enhance the efficiency of MDC are proposed. Rather, it is recognised that the government has signalled its intention to offer GPs financial incentives for providing co-ordinated care, a move that aims to enhance the efficiency of primary care models such as MDC (Sowada, Sagan and Kowalska-Bobko, 2019[]).

MDC performs well against the equity best practice criterion given its ability to reach patients living outside urban areas. In order to improve equity, information on access to and impact of MDC on different priority population groups is needed (as explored under “Enhancing the evidence base”).

More robust evaluations are necessary to understand the real impact of MDC. Data to evaluate the impact of MDC relied on cross-sectional utilisation data for MDC patients only. Therefore, results from the analysis only provide an indication of MDC’s impact (see “Effectiveness”). Future evaluation study designs should consider:

  • Collecting data for a control group, for example using patient data from another region in Poland

  • Controlling for potential confounding variables, that is, variables that impact the outcome of interest (e.g. patients with a lower socio-economic status typically experience worse health outcomes)

  • Assessing the impact using data on avoidable hospital admissions – e.g. for diabetes, congestive heart failure and chronic obstructive pulmonary disease – given it is common indicator for assessing primary care quality.

Stratify patient data to measure the impact of MDC on priority population groups. When studying the impact of healthcare interventions, it is important to look at their effect on inequalities. As a first step, it is necessary to identify potential inequalities, which can be captured during the data collection process (e.g. collect patient data on race, socio-economic groups if allowed and feasible). This information allows researchers to analyse whether the intervention increases or decreases inequalities. If the latter, follow-up research, for example, through patient interviews, will help MDC administrators adapt and improve the intervention to suit the needs of priority populations.

Given limited information on the extent of coverage for MDC, specific polices to boost uptake have not been included. However, in general, efforts to boost health literacy (HL) likely increase patient motivation to take control of their health and thus participate in programs such as MDC (see Box 8.4 for example policies to boost HL).

This section explores the transferability of MDC and is broken into three components: 1) an examination of previous transfers; 2) a transferability assessment using publicly available data; and 3) additional considerations for policy makers interested in transferring MDC.

New models of primary care have been transferred across many OECD countries and EU Member States. The MDC model reflects best practice principles in the area of primary care, specifically by delivering patient centred care through a co-ordinated team of health professionals. These “new models of [primary] care” exist in several OECD/EU countries with Australia (Primary Health Networks), Canada (My Health Team) and the United States (Comprehensive Primary Care Plus) leading the way (OECD, 2020[]).

The following section outlines the methodological framework to assess transferability and results from the assessment.

Details on the methodological framework to assess transferability can be found in Annex A.

Several indicators to assess the transferability of MDC were identified (Table 8.2). Indicators were drawn from international databases and surveys to maximise coverage across OECD and non-OECD European countries. Please note, the assessment is intentionally high level given the availability of public data covering OECD and non-OECD European countries.

The transferability of MDC was assessed using six indicators covering three contextual factors – the population context, the sector context (primary care) and the economic context (Table 8.3). Results from the assessment indicate primary care systems in target countries would be supportive of MDC – for example, in countries with available data, 79% of primary care physicians utilise EHRs, a key tool to support co-ordinated care, compared to just 30% in Poland. Further, most countries have the same or a lower proportion of GPs working in single practices, which is a proxy measure of GP willingness to work in a team. MDC’s extent of coverage is expected to be high given people living in other OECD/non-OECD European countries are more likely to access primary care (i.e. GP) (79% of people of in OECD/non-OECD European countries visited a GP in the last year compared to 64% in Poland). Nevertheless, results from the assessment indicate many countries may face barriers to implement co-ordinated care given only 22% report extensive task shifting between primary care physicians and nurses. An indicator to measure political support is not included in the assessment. However, the recent (2018) agreement on the Declaration of Astana clearly shows countries support efforts to improve primary care.

It is important to note that 17 OECD countries have implemented new models of primary care similar to MDC (OECD, 2020[]). For these countries, results from the transferability assessment can instead be used to identify areas to enhance the impact of the new care model. For example, despite establishing Primary Care Units, a high proportion of GPs in Austria continue to work in single practices.

To help consolidate findings from the transferability assessment above, countries have been clustered into one of three groups, based on indicators reported in Table 8.2. Countries in clusters with more positive values have the greatest transfer potential. For further details on the methodological approach used, please refer to Annex A.

Key findings from each of the clusters are below with further details in Figure 8.8 and Table 8.4:

  • Countries in cluster one have populations who frequently see their GP and a primary care system equipped to implement this model of care. Further, they spend relatively more on primary care. For these reasons, these countries are les likely to experience any implementation barriers should this intervention be transferred.

  • Countries in cluster two also have populations who frequently attend their GP, however, they spend relatively less on primary care indicating potential long-term affordability issues.

  • Countries in cluster three have populations who are less likely visit their GP and operate primary care systems that may not encourage integration among different care sectors. It is important to note that Poland falls under this cluster, meaning conditions in which these clusters could improve on, although ideal, are not pre-requisites. For example, Poland introduced this model of care as it recognised its primary care sector was weak.

Data from publicly available datasets is not ideal to assess the transferability of MDC. For example, there is no international comparable data measuring the level of trust between primary care professionals. Therefore, Box 8.5 outlines several new indicators policy makers could consider before transferring MDC.

MDC offers patient-centred, co-ordinated care. MDC stratifies patients into risk groups based on information collected from their main complex visit. Data from the main complex visit is subsequently uploaded into an Individual Medical Care Plan, which is available to the patient’s therapeutic care team. In addition, each patient is assigned a care co-ordinator who sets up necessary appointments and educational sessions to enhance self-management. According to the definition in OECD’s recent primary care report, MDC is a “new model of care”” as it delivers care through a multidisciplinary team and promotes shared-decision making (OECD, 2020[]).

New models of primary care, such as MDC, reduce healthcare use and improve patient reported experience and outcomes. Healthcare utilisation data over the period 2014-20 show that despite an increase in the average age of MDC patients, the number of GP and specialists visits per person did not change markedly (and in some cases declined). Given the data do not control for confounding factors nor include a control group, results from this analysis only provide an indication of MDC’s impact on utilisation. An evaluation of the PHC Plus pilot (which covered 41 primary care facilities, including MDC) found patients enrolled in the programme reported better experience and outcome measures. Results regarding utilisation did not see a reduction in hospital utilisation.

MDC successfully reaches patients living outside urban areas who typically have lower levels of access to care. More than half (61%) of all MDC patients are located outside densely populated areas, most of whom live in thinly populated areas. Further, MDC requires specialist physicians to visit small rural health centres as a way to address geographic health inequalities. For these reasons, MDC performs particularly well against the equity best practice criterion.

Better use of digital tools such as EHRs and health portals will enhance the performance of MDC. MDC aims to provide patients with co-ordinated patient-centred care. Digital tools such as EHRs and health portals play a key role in this context, and as such have been continually promoted in Poland in recent years. Policy makers should therefore continue their efforts to build the country’s digital health system.

Primary care models similar to MDC exist in many OECD countries, with this number likely to grow. MDC represents a new model of primary care that promotes patient-centred, co-ordinated, multidisciplinary care. OECD’s recent primary care report found 17 OECD countries employ this type of model indicating it is highly transferable.

Next steps for policy makers and funding agencies regarding the MDC model are in Box 8.6.

References

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[8] Effective Public Health Pratice Project (1998), Quality assessment tool for quantitative studies, https://www.nccmt.ca/knowledge-repositories/search/14.

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[15] Maier, C. and L. Aiken (2016), “Task shifting from physicians to nurses in primary care in 39 countries: a cross-country comparative study”, The European Journal of Public Health, Vol. 26/6, pp. 927-934, https://doi.org/10.1093/eurpub/ckw098.

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Notes

← 1. MDC differs from other pilot sites in two key aspects: MDC offers care for all patients where as PHC Plus pilots cover patients with one of 11 selected diseases, further, MDC incorporated an oncology prevention element.

← 2. The relatively sharp decline in gynaecological appointments reflects three factors: 1) an increase in prevention activities between years 2011-15; 2) better co-ordination and management of gynaecological appointments; and 3) since 2017, midwives in Poland have the right to provide care for pregnant women independently.

← 3. The algorithm uses information such as diagnostic classification, date of diagnosis, age and gender.

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