The Adoption Level of Electronic Health Records in Turkish Hospitals and the Relation with Hospital Sizes

doi:10.21203/rs.2.17869/v1

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Research article

The Adoption Level of Electronic Health Records in Turkish Hospitals and the Relation with Hospital Sizes

https://doi.org/10.21203/rs.2.17869/v1

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published 21 Oct, 2020

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Background Considering the benefits of using electronic health records (EHR) for maintaining the overall quality of clinical care, the nationwide adoption of EHR in hospitals has become a policy priority. The electronic medical record maturity model (EMRAM) is one of the most popular survey tools developed by the Healthcare Information and Management Systems Society (HIMSS) that measures the level of adoption for EHR functions in a hospital or a secondary care setting. This study aims to measure the digital capacity of public hospitals in Turkey and criticize the relation between adoption and hospital size.

Methods EMRAM surveys were completed by 600 (68.9%) public hospitals of Turkey between 2014 and 2017. The availability and prevalence of medical information systems and EHR functions were measured. The association between hospital size and the availability/prevalence of EHR functions was also calculated.

Results We found that 63.1% of all hospitals in Turkey have at least basic EHR functions, and 36% have comprehensive EHR functions, which is better than the results of Korean hospitals of 2017 but still lower than the USA hospitals of 2015 (1)[1]and 2017. Our findings suggest that small hospitals are better than larger hospitals at adopting certain EHR functions.

Conclusion Measuring the overall adoption level of EHR functions is an emerging approach and a beneficial tool for the strategic management of countries. This study is the first one covering all public hospitals in a country by using EMRAM. The results are used by MoH of Turkey to disseminate the benefits of EHR functions overall in the country.

Health Policy

Electronic health records

meaningful use

CPOE

PACS

eMAR

CDSS

EMRAM

HIMSS

Electronic health records (EHR) are electronic data files that can be stored, accessed, and transferred electronically by information systems. EHR covers the episodic health information of a patient staying in a medical facility. This information repository, when used in a meaningful manner, keeps all of the records that are useful, effective, ethical, and easily accessible within regulated boundaries (2,3). The key functions contained in EHR are computerized physician order entries (CPOE) (4–7), closed-loop medication administrations (CLMA) (8–12), clinical decision support systems (CDSS) (13–15), picture archiving and communication systems (PACS) (16), and electronic medication administration records (eMAR) (9,17,18).

1.1. Overall Electronic Health Record Capabilities of Countries

There are only a few studies evaluating the national adoption level of EHR functions in hospitals. One of the first studies was conducted in 2009 by Jha et al., which focused on hospitals in the USA (19). The survey developed by Jha et al. questioned the availability of 24 EHR functions. They classified the results according to whether the hospitals had basic or comprehensive EHR functions. The basic EHR functions indicate that clinical documentation, CPOE, CDSS, laboratory and imaging results are applicable in one clinic, while comprehensive EHR functions, means all those functions are available in all clinics of the hospital. This study showed that only 1.5% of US hospitals had comprehensive EHR functions, and 7.6% had basic EHR functions. The next study, which was also conducted by Jha et al. in 2011, indicated that the total percentage of hospitals having at least basic EHR functions had increased to 15.1% in the USA (20). The most recent studies were conducted in the US by Adler-Milstein et al. in 2014 (21), 2015 (1), and 2017 (22). These studies showed that the proportion of hospitals having comprehensive EHR functions was 25.5% in 2014, 34.1% in 2015, and 39.1% in 2017. Similarly, the proportion of hospitals having at least basic EHR functions was 58.9% in 2014, 75.2% in 2015, and 80.5% in 2017.

Studies focusing on Korean hospitals are also remarkable. The first study was conducted by Park et al. in 2005 (23). The researchers used the survey designed by Ash et al. (24), which is mostly focusing on CPOE rather than other EHR functions. This study showed that although 80.3% of hospitals have CPOE forms, only 9% have complete EHR systems. The next two studies conducted in Korean hospitals used the survey developed by Jha et al. (19), and so that they could compare their results with hospitals in the USA. The 2012 study by Yoon et al. (25) showed that the percentage of Korean hospitals having at least basic EHR functions was 37.2%, which was better than the proportion of hospitals in the USA (15.1%). The most recent study, which was published in 2017 by Kim et al. (26), showed that the percentage of Korean hospitals having at least basic EHR functions increased to 58.1%, but this figure was lower than the proportion of hospitals in the US with at least basic EHR functions (80.5%) for the same year. The rapid increase in the EHR adoption level of hospitals in the USA may be attributed to the financial and political support in line with the HITECH Act (2009).

Another remarkable study was published in 2014 by Shu et al. (27). This cross-sectional study measured the percentage of EHR adoption in tertiary hospitals in China. The authors conducted a national survey entitled the Model of EHR Grading (MEG), which gives hospitals a rating between Stage 0 and 7 based on their adoption of 37 EHR functions. This study showed that, among 848 hospitals, 30.7% were Stage 0, 12.0% were Stage 1, 31.7% were Stage 2, 22.2% were Stage 3, 2.7% were Stage 4, 0.6% were Stage 5, 0.1% were Stage 6, and 0% were Stage 7. Other studies performed in Greece (28) and Saudi Arabia (29) are not survey-based but thematic reviews and focusing on giving insights for the policymakers.

1.2. Electronic Health Record Capability in Turkey

The Ministry of Health (MoH) in Turkey launched the Health Transformation Program in 2003 (30) and finalized its first phase in 2017. There were many milestones achieved with this program in terms of using information and technology standards such as the National Health Data Dictionary (31), Health Coding Reference Server (32), ICD 10 (33), HL7 (34,35), National Health Tariffs (36), and systems such as The Family Physician Information System (37), National Health Information System (31), The Central Physician Booking System (38), Central Claim Management System (39), and Personal Health Record (40,41).

After establishing this infrastructure and those applications, the MoH of Turkey intended to measure the overall adoption level of EHR functions in public hospitals. Therefore, one of the objectives of the Strategic Plan of the MoH between 2013 and 2017 was to study the “digital hospital” concept and disseminate it across all public hospitals (42). In line with this strategic objective, MoH signed a collaborative agreement with the Healthcare Information and Management Systems Society (HIMSS) (43).

1.3. Electronic Medical Health Record Adaptation Model

HIMSS developed the electronic medical health record adaptation model (EMRAM) (44). EMRAM provides algorithms to assess inpatient services in acute hospitals based on their EHR capabilities and, like all other models (45–47) created by HIMSS, has eight stages from 0 to 7. The requirements to be met by the hospitals in our study are shown in Table 1 (44).

In order to benchmark this study with the previous ones conducted in the USA and Korean hospitals, EMRAM requirements may be aligned with the 24 functions of the survey developed by Jha et al. (19). Therefore, Stage 3 of EMRAM indicates that the hospital has basic EHR functions, and Stage 6 indicates that the hospital has comprehensive EHR functions.

Table 1. HIMSS EMRAM Requirements (until the 1^st of January 2018)

Stage	Cumulative Capabilities
Stage 7	Complete Electronic Medical Record (EMR), Continuity of Care Document transactions to share data; Data warehousing; Data Continuity with Emergency Department, ambulatory, OP
Stage 6 (comprehensive EHR functions)	Physician documentation (structured templates), full Clinical Decision Support System (CDSS) (variance & compliance), full Remote-PACS
Stage 5	Closed-loop medication administration
Stage 4	Computerized Physician Order Entry (CPOE), CDSS (clinical protocols)
Stage 3 (basic EHR functions)	Nursing/clinical documentation (flow sheets), CDSS (error checking), PACS available outside Radiology
Stage 2	Clinical Data Repository (CDR), Controlled Medical Vocabulary, CDSS, may have Document Imaging; Health Information Exchange (HIE) capable
Stage 1	Ancillaries - Lab, Rad, Pharmacy - All Installed
Stage 0	All Three Ancillaries not Installed

This study utilizes the EMRAM survey, including five dimensions: i) Software Applications, ii) Software Usage, iii) Electronic Ordering, iv) Image Management Systems, and v) Medication Safety.

The survey studies took place between 2014 and 2017. Sixteen regional workshops were organized in the same period, with 2,716 participants, including hospital managers. This study aimed to cover all 870 public hospitals (48) in Turkey. The inconsistent or havin poor quality surveys were excluded from this study. Only the most recent one was analyzed if there is more than one survey from the same hospital.

IBM SPSS version 23 was used to perform statistical analyses. The Chi-Square test was conducted to analyze associations or differences between the categorical variables, such as hospital size and the usage/prevalence of EHR functions. The threshold for significance was set at α = 0.05.

A total of 889 surveys were collected from 870 hospitals. Two hundred four of them were excluded because they are repeated submissions. The number of distinct hospitals included in the study is 685 (78.7%). Similarly, 85 of the surveys were also excluded because of poor quality data and some severe inconsistencies. Overall, 600 verified, acceptable surveys (68.9%) were analyzed in this study. The distribution of the hospitals and their size, type, and level of healthcare are shown in Table 2.

Table 2. Basic characteristics of responding hospitals

Characteristic	Number	Percentage (%)
Region	7	100
Province	79	98
Hospital	600	68.3

Size
Small (0-99 beds)	295	49.2
Medium (100-399 beds)	205	34.1
Large (≥400 beds)	100	16.7

Teaching Status
Public Hospital	531	88.5
Training Hospital (including affiliated university hospitals)	69	11.5

Hospital Type
Secondary Hospital	489	81.5
Tertiary Hospital	54	9
Branch Hospital	57	9.5

The distribution of the number of hospitals in each EMRAM stage is shown in Figure 1. Although none of the hospitals surveyed were Stage 0 or 1, 36.83% of the hospitals were classified as Stage 2. It is logical to suggest that the barrier requirements of Stage 3, such as having PACS, eMAR, and adopting nursing documents, explain the high percentage of Stage 2 hospitals. It seems, once hospitals achieve Stage 3, they may be able to adopt the technologies to advance to later stages.

Figure 1. Distributions of hospitals concerning EMRAM Stages

3.1. Availability of Applications and Electronic Health Record Functions

This section provides the results of the availability of information systems and EHR functions in hospitals.

3.1.1. Hospital Information Systems, Laboratory Information Systems, and Patient Administration Systems

The availability of HIS, LIS, and PAS is listed in Table 3 concerning the hospital sizes. The results show that 100% of hospitals have an HIS suite. Similarly, 94% of hospitals have PAS, and 93% of hospitals have LIS integrated with HIS.

Table 3. Availability of HIS, LIS and PAS

Applications	Hospital Size	Live	Live - hospital-wide	Live – departmental	Not Automated	Not Reported	Total
EMR / Hospital Information System (Suite)	Large (>=400 beds)	100 (100%)	0 (0.0%)	0 (0.0%)	0 (0.0%)	0	100
	Medium (100-399 beds)	205 (100%)	0 (0.0%)	0 (0.0%)	0 (0.0%)	0	205
	Small (6-99 beds)	294 (99.6%)	1 (0.34%)	0 (0.0%)	0 (0.0%)	0	295
	Percentage	99.8%	0.2%	0.0%	0.0%	0.0%	100%
	Total	599 (99.8%)	1 (0.16%)	0 (0.0%)	0 (0.0%)	0	600
Patient Administration System	Large (>=400 beds)	1 (1%)	98 (98%)	0 (0.0%)	1 (1%)	0	100
	Medium (100-399 beds)	2 (0.99%)	189 (93.56%)	3 (1.48%)	8 (3.96%)	3	205
	Small (6-99 beds)	5 (1.70%)	269 (91.80%)	9 (3.07%)	10 (3.41%)	2	295
	Percentage	1.3%	92.7%	2.0%	3.2%	0.8%	100%
	Total	8 (1.34%)	556 (93.44%)	12 (2.01%)	19 (3.19%)	5	600
Laboratory Information System	Large (>=400 beds)	41 (41%)	57 (57%)	2 (2%)	0 (0.0%)	0	100
	Medium (100-399 beds)	43 (21.07%)	153 (75%)	7 (3.43%)	1 (0.49%)	1	205
	Small (6-99 beds)	7 (2.37%)	257 (87.11%)	29 (9.83%)	2 (0.67%)	0	295
	Percentage	15.2%	77.8%	6.3%	0.5%	0.2%	100%
	Total	91 (15.19%)	467 (77.96%)	38 (6.34%)	3 (0.5%)	1	600

3.1.2. Clinical Documents and Computerized Physician Order Entry

The clinical documentation is also provided in Table 4, along with hospital sizes. The results show that 98.6% of hospitals have a clinical data repository, and 79.7% of hospitals have a hospital-wide clinical data repository. Physician and nursing documents have very similar availability percentage across hospital sizes. Although 86.2% of hospitals have physician documents, the nursing documents are available in 84.8% of all hospitals. However, CPOE is not functional in 13.8% of hospitals. The size of the hospital has no significant relationship with the availability of clinical documents and CPOE systems.

Table 4. Availability of clinical documents and CPOE systems

Applications	Hospital Size	Live	Live - hospital-wide	Live - departmental	Not Automated	Not Reported	pl
Clinical Data Repository	Large (>=400 beds)	42 (42%)	56 (56%)	1 (1%)	1 (1%)	0	p<0.001
	Medium (100-399 beds)	44 (21.5%)	154 (75.1%)	4 (2%)	3 (1.5%)	0
	Small (6-99 beds)	7 (2.4%)	268 (91.2%)	16 (5.4%)	3 (1%)	1
	Percentage	15.5%	79.7%	3.5%	1.2%	0.2%
	Total	93	478	21	7	1
Nursing Documentation	Large (>=400 beds)	24 (24%)	55 (55%)	2 (2%)	19 (19%)	0	p<0.001
	Medium (100-399 beds)	25 (12.3%)	131 (64.5%)	10 (4.9%)	37 (18.2%)	2
	Small (6-99 beds)	6 (2%)	230 (78.2%)	26 (8.8%)	32 (10.9%)	1
	Percentage	9.2%	69.3%	6.3%	14.7%	0.5%
	Total	55	416	38	88	3
Physician Documentation	Large (>=400 beds)	28 (28%)	53 (53%)	3 (3%)	16 (16%)	0	p<0.001
	Medium (100-399 beds)	32 (15.8%)	128 (63.1%)	11 (5.4%)	32 (15.8%)	2
	Small (6-99 beds)	6 (2.0%)	234 (79.6%)	22 (7.5%)	32 (10.9%)	1
	Percentage	11.0%	69.2%	6.0%	13.3%	0.5%
	Total	66	415	36	80	3
CPOE	Large (>=400 beds)	25 (25%)	55 (55%)	2 (2%)	18 (18%)	0	p<0.001
	Medium (100-399 beds)	31 (15.1%)	132 (64.4%)	8 (3.9%)	34 (16.6%)	0
	Small (6-99 beds)	3 (1%)	234 (79.6%)	26 (8.8%)	31 (10.5%)	1
	Percentage	9.8%	70.2%	6.0%	13.8%	0.2%
	Total	59	421	36	83	1

3.1.4. Medication Administration

Pharmacy and medication administration are essential functions of HIS. As depicted in Table 5, the surveys indicate that 99.5% of all hospitals have a pharmacy management system, even though three (0.5%) of them receive this service from an external source. The medications applied to the patient are recorded during the time of application in 66% of the hospitals, and 29.2% of the remaining do not record those applications.

Table 5. Availability of medication administration systems

Applications	Hospital Size	Live	Live –hospital-wide	Live - departmental	Installation in Process	Not Automated	ESP attached	Not Reported	Total
Pharmacy Management System	Large (>=400 beds)	41 (41%)	55 (55%)	4 (4%)	0 (0%)	0 (0%)	0 (0%)	0	100
	Medium (100-399 beds)	43 (20.97%)	144 (70.24%)	15 (7.3%)	0 (0%)	2 (0.97%)	1 (0.48%)	0	205
	Small (6-99 beds)	8 (2.71%)	247 (83.72)	37 (12.54%)	0 (0%)	1 (0.33%)	2 (0.67%)	0	295
	Percentage	15.3%	74.3%	9.3%	0.0%	0.5%	0.5%	0.0%	100%
	Total	92	446	56	0	3	3	0	600
Electronic Medication Administration Record	Large (>=400 beds)	30 (30%)	46 (46%)	4 (4%)	1 (1%)	19 (19%)	0 (0%)	0	100
	Medium (100-399 beds)	29 (14.28%)	110 (54.18%)	3 (1.47%)	0 (0%)	61 (30.04%)	0 (0%)	2	205
	Small (6-99 beds)	5 (1.70%)	176 (60.06%)	17 (5.80%)	0 (0%)	95 (32.42%)	0 (0%)	2	295
	Percentage	10.7%	55.3%	4.0%	0.2%	29.2%	0.0%	0.7%	100%
	Total	64	332	24	1	175	0	4	600

3.1.5. Image Management

As depicted in Table 6, the surveys show that 89% of hospitals have a PACS system; however, the PACS systems in 14.5% of hospitals are not integrated with HIS and may only the by the radiology department. On the other hand, 9.5% of hospitals have a dictation and speech recognition system, which helps the radiologists write their reports more efficiently.

Table 6. Availability of image management systems

Applications	Hospital Size	Live	Live - hospital-wide	Live - departmental	Installation in Process	Service Not Provided	Not Automated	Not Reported	Total
Dictation with Speech Recognition	Large (>=400 beds)	1 (1%)	6 (6%)	6 (6%)	0 (0.0%)	0 (0.0%)	46 (46%)	41	100
	Medium (100-399 beds)	1 (0.64%)	15 (9.61%)	5 (3.20%)	0 (0.0%)	0 (0.0%)	135 (86.53%)	49	205
	Small (6-99 beds)	1 (0.35%)	16 (5.65%)	6 (2.12%)	0 (0.0%)	0 (0.0%)	260 (91.87%)	12	295
	Percentage	0.5%	6.2%	2.8%	0.0%	0.0%	73.5%	17.0%	100%
	Total	3	37	17	0	0	441	102	600
Radiology - Central PACS	Large (>=400 beds)	31 (31%)	56 (56%)	1 (1%)	1 (1%)	1 (1%)	9 (9%)	1	100
	Medium (100-399 beds)	34 (16.58)	137 (66.8%)	24 (11.70%)	0 (0.0%)	1 (0.48%)	9 (4.39%)	0	205
	Small (6-99 beds)	4 (1.36%)	185 (63.13%)	62 (21.16%)	0 (0.0%)	1 (0.34%)	41 (13.99%)	2	295
	Percentage	11.5%	63.0%	14.5%	0.2%	0.5%	9.8%	0.5%	100%
	Total	69	378	87	1	3	59	3	600

3.2. Usage of Applications and Electronic Health Record Functions

This section provides the results of the usage and dissemination of information systems and EHR functions in hospitals.

3.2.1. Clinical Documents and Computerized Physician Order Entry

As shown in Table 4, physician documents are available in 84.6% of hospitals. Moreover, we found significant variations in the prevalence of physician documents (Table 7). A vast majority of hospitals (79.4%) have electronic medical record systems, and 74.5% of hospitals are using physician documents in at least 50% of the hospital. The ratio-generating discrete data obtained from the physician documents are also other critical indicators that represent the capacity to extract information from the medical data. We found that 75.2% of the hospitals using physician documents are using discrete data in physician documents in at least 50% of the hospital.

Table 7. Prevalence of physician documents

SW Usage	Hospital Size	51-100%	1-50%	Not Applicable	Total	Not Reported	p
What percent of all current medical records are electronic (incl. digital/scanned data)?	Large (>=400 beds)	59 (59%)	0 (0.0%)	0 (0.0%)	100	41	0.017*
	Medium (100-399 beds)	152 (74.14%)	9 (4.39%)	0 (0.0%)	205	44
	Small (6-99 beds)	265 (89.83%)	29 (9.83%)	0 (0.0%)	295	1
	Percentage	79.3%	6.3%	0.0%	100.0%	14.3%
	Total	476	38	0	600	86
What percent of Physician Documentation generates discrete (computer-readable) data?	Large (>=400 beds)	71 (71%)	6 (6%)	16 (16%)	100	7	0.321
	Medium (100-399 beds)	150 (73.17%)	13 (6.34%)	34 (16.58%)	205	8
	Small (6-99 beds)	230 (77.96%)	24 (82.75%)	32 (93.60%)	295	9
	Percentage	75.2%	7.2%	13.7%	100.0%	4.0%
	Total	451	43	82	600	24
What percent of physicians use the Physician Documentation system?	Large (>=400 beds)	73 (73%)	6 (6%)	16 (16%)	100	5	0.826
	Medium (100-399 beds)	149 (72.68%)	16 (7.80%)	34 (16.58%)	205	6
	Small (6-99 beds)	225 (92.8%)	26 (8.81%)	41 (43.15%)	295	3
	Percentage	74.5%	8.0%	15.2%	100.0%	2.3%
	Total	447	48	91	600	14

*p<0.05 **p<0.01

Electronic ordering also has similar prevalence. The usage of CPOE is 72.5% for medication and 62.7% for non-medication orders, respectively. However, orders for nurses in inpatient care facilities have a slightly higher proportion of 79%. By considering the prevalence of CPOE, Table 9 shows that the percentage of CPOE usage for inpatient medication orders is 66.6% and 70.5% for non-medication orders in more than 50% of the hospital.

Although there is no significant relationship between hospital size and the usage of electronic ordering (Table 8), the prevalence of electronic ordering has a significant relationship with hospital size (Table 9). The results show that small hospitals are better than medium-sized and larger hospitals in adopting electronic ordering capabilities.

Table 8. Availability of electronic ordering

Questions	Hospital Size	Yes	No	Not Applicable	Not Reported	p
Electronic ordering for medication	Large (>=400 beds)	54 (90.0%)	5 (8.3%)	1 (1.7%)	40	0.004**
	Medium (100-399 beds)	140 (85.9%)	11 (6.7%)	12 (7.4%)	42
	Small (6-99 beds)	241	41	5	8
	Percentage	72.5%	9.5%	3.0%	15.0%
	Total	435	57	18	90
Electronic ordering for non-medication	Large (>=400 beds)	41 (97.6%)	1 (2.4%)	0 (0.0%)	58	0.016*
	Medium (100-399 beds)	90 (98.9%)	1 (1.1%)	0 (0.0%)	114
	Small (6-99 beds)	245 (91.4%)	23 (8.6%)	0 (0.0%)	27
	Percentage	62.7%	4.2%	0.0%	33.2%
	Total	376	25	0	199
Electronic ordering for nursing and/or physician services	Large (>=400 beds)	75 (76.5%)	5 (5.1%)	18 (18.4%)	2	0.001**
	Medium (100-399 beds)	160 (78.4%)	17 (8.3%)	27 (13.2%)	1
	Small (6-99 beds)	239 (81.3%)	37 (12.6%)	18 (6.1%)	1
	Percentage	79.0%	9.8%	10.5%	0.7%
	Total	474	59	63	4

*p<0,05 **p<0,01

Table 9. Prevalence of electronic ordering

Questions	Hospital Size	100% (all)	76-100%	51-75%	26-50%	1-25%	Not Applicable	Not Reported	p
What % of all inpatient medication orders are processed electronically?	Large (>=400 beds)	0	48 (81.35%)	3 (5.08%)	1 (1.69%)	2 (3.38%)	5 (8.47%)	41	0.856
	Medium (100-399 beds)	0	123 (81.45%)	9 (5.96%)	2 (1.32%)	4 (2.64%)	13 (8.6%)	54
	Small (6-99 beds)	0	203 (71.22%)	14 (4.91%)	9 (3.15%)	12 (4.21%)	47 (16.49%)	10
	Percentage	0.0%	62.3%	4.3%	2.0%	3.0%	10.8%	17.5%
	Total	0	374	26	12	18	65	105
What % of all inpatient non-medication orders are processed electronically?	Large (>=400 beds)	0	63 (66.31%)	5 (5.26%)	4 (4.21%)	4 (4.21%)	19 (20%)	5	0.586
	Medium (100-399 beds)	0	125 (62.18%)	17 (8.45%)	10 (4.97%)	11 (5.47%)	38 (18.90%)	4
	Small (6-99 beds)	0	180 (61.22%)	33 (11.22%)	19 (6.46%)	20 (6.80%)	42 (14.28%)	1
	Percentage	0.0%	61.3%	9.2%	5.5%	5.8%	16.5%	1.7%
	Total	0	368	55	33	35	99	10

3.2.2. Clinical Decision Support

As shown in Table 10, the hospitals have access to clinical decision support systems (CDSS). Correctly, CDSS was used in 69% of physician/nursing documents, 71.7% of medication orders, and 57.3% of non-medication orders. Additionally, although there is no significant relationship between hospital size and the usage of CDSS in clinical documents and non-medication orders, the usage of CDSS in medication orders has a significant relationship with hospital size. Small hospitals are better than medium-sized and larger hospitals in adopting CDSS for medication orders.

Table 10. Usage of CDSS

Questions	Hospital Size	Yes	No	Not Applicable	Not Reported	P
Clinical Documentation (Physician / Nursing Documentation)	Large (>=400 beds)	63 (65.6%)	6 (6.3%)	27 (28.1%)	4	p<0.001
	Medium (100-399 beds)	131 (64.5%)	23 (11.3%)	49 (24.1%)	2
	Small (6-99 beds)	220 (75.9%)	37 (12.8%)	33 (11.4%)	5
	Percentage	69.0%	11.0%	18.2%	1.8%
	Total	414	66	109	11
Medication Orders	Large (>=400 beds)	64 (66.0%)	4 (4.1%)	29 (29.9%)	6	0.087
	Medium (100-399 beds)	144 (71.6%)	9 (4.5%)	48 (23.8%)	4
	Small (6-99 beds)	225 (76.5%)	18 (6.1%)	51 (17.3%)	1
	Percentage	71.7%	5.2%	21.3%	1.8%
	Total	430	31	128	11
Non-Medication Orders	Large (>=400 beds)	48 (85.7%)	7 (12.5%)	1 (1.8%)	44	p<0.001
	Medium (100-399 beds)	114 (72.2%)	43 (27.2%)	1 (0.6%)	47
	Small (6-99 beds)	182 (65.7%)	71 (25.6%)	24 (8..7%)	18
	Percentage	57.3%	20.2%	4.3%	18.2%
	Total	344	121	26	109

3.2.3. Closed-Loop Medication Administration

Table 11 shows that 69.2% of hospitals have a second line of validation for the prescriptions by pharmacists before the drug is delivered to wards/patient. On the other hand, only 0.8% of hospitals have an automatic dispensing system for drugs. That means that the drugs are delivered from pharmacies to the wards and then from the ward station to the bedside by the staff via trolleys/carts. This method is acceptable for drug safety, even if it is not very time-efficient.

Furthermore, the survey suggests that only 45% of hospitals apply the assistance of technology at the bedside when administering medicines (i.e., barcode and RFID) which is a method of electronically confirming the “five right rule”, which are right patient, right drug, right dose, right time and right path (49,50). Similarly, only 41.3% of the hospitals are immediately recording the applied drugs at the bedside, which means that the nurses are recording the applied drugs at the ward station after the drug application has occurred. Interestingly, there is no significant relationship between hospital size and usage of closed-loop medication administration (CLMA) functions, except “the second line of validation for medical prescriptions, which is documented electronically.” Table 11 indicates that small hospitals are better than larger hospitals and provide a second line validation of prescriptions electronically.

Table 11. Usage of CLMA

Questions	Hospital Size	Yes	No	Not Applicable	Not Reported	p
2^nd line of validation for medication prescriptions which is documented electronically	Large (>=400 beds)	53 (88.3%)	7 (11.7%)	0 (0.0%)	40	0.271
	Medium (100-399 beds)	132 (80.5%)	32 (19.5%)	0 (0.0%)	41
	Small (6-99 beds)	230 (79.3%)	60 (20.7%)	0 (0.0%)	5
	Percentage	69.2%	16.5%	0.0%	14.3%
	Total	415	99	0	86
Automated Dispensing of medication is available	Large (>=400 beds)	3 (5.0%)	57 (95.0%)	0 (0.0%)	40	0.022*
	Medium (100-399 beds)	1 (0.6%)	160 (99.4%)	0 (0.0%)	44
	Small (6-99 beds)	1 (0.3%)	291 (99.7%)	0 (0.0%)	3
	Percentage	0.8%	84.7%	0.0%	14.5%
	Total	5	508	0	87
Closed-loop medication administration at the point of care	Large (>=400 beds)	60 (75.9%)	19 (24.1%)	0 (0.0%)	21	p<0.001
	Medium (100-399 beds)	117 (62.9)	69 (37.1%)	0 (0.0%)	19
	Small (6-99 beds)	93 (31.8%)	199 (68.2%)	0 (0.0%)	2
	Percentage	45.0%	47.8%	0.0%	7.0%
	Total	270	287	0	42
Electronic Medication Administration Record (EMAR) available at point of care/bedside?	Large (>=400 beds)	44 (44.0%)	18 (18.0%)	38 (38.0%)	0	p<0.001
	Medium (100-399 beds)	99 (48.8%)	65 (32.0%)	39 (19.2%)	2
	Small (6-99 beds)	105 (36.6%)	181 (63.1%)	1 (0.3%)	7
	Percentage	41.3%	44.0%	13.0%	1.5%
	Total	248	264	78	9

Table 12 shows the items or persons (i.e., patient and nurse) to be identified using technology (RFID and barcode) during the application of medication at the bedside. The results show that the technology is used more frequently to identify medications and patients than identifying nurses. However, although those percentages are high in large and medium hospitals, the percentages are significantly lower in small hospitals. Moreover, the p-value indicates that there is a significant relationship between the auto-identified target (medication, nurse, and patient) and usage of technology.

Table 12. Usage of five right rules at the bedside

Questions	Hospital Size	Auto-identified	Yes	No	Not Reported	P
Which of the following is auto-identified during bedside medication administration?	Large (>=400 beds)	Medication	58 (69.0%)	26 (31.0%)	16	0.028*
		Nurse	43 (51.2%)	41 (48.8%)	16
		Patient	57 (67.9%)	27 (31.1%)	16
		Percentage	52.7%	31.3%	16.0%
		Total	158	94	48
	Medium (100-399 beds)	Medication	124 (61.7%)	77 (38.3%)	4	0.003**
		Nurse	95 (47.3%)	106 (52.7%)	4
		Patient	125 (62.2%)	76 (37.8%)	4
		Percentage	55.9%	42.1%	2.0%
		Total	344	259	12
	Small (6-99 beds)	Medication	104 (35.5%)	189 (64.5%)	2	0.422
		Nurse	99 (33.8%)	194 (66.2%)	2
		Patient	114 (38.9%)	179 (61.1%)	2
		Percentage	35.8%	63.5%	0.7%
		Total	317	562	6

3.2.4. Image Management

Image management systems (IMS) are stand-alone applications that are required to be integrated with EHR or HIS for practical usage. Table 13 shows that 74.7% of hospitals integrated their IMS with EHR. When we consider the prevalence of IMS in hospitals, we can see that 37.3% of hospitals are using IMS in greater than 50% of the entire facility. There is no significant relationship between hospital size and the percentage of IMS integration with EHR nor the prevalence of IMS.

Table 13. IMS Integration with EMR

Question	Hospital Size	Yes	No	Not Applicable	Not Reported	p
Is your IMS solution integrated with your Electronic Medical Record (EMR)?	Large (>=400 beds)	57 (95.0%)	1 (1.7%)	2 (3.3%)	40	p<0.001
	Medium (100-399 beds)	155 (95.7%)	4 (2.5%)	3 (1.9%)	43
	Small (6-99 beds)	236 (81.7%)	10 (3.5%)	43 (14.9%)	6
	Percentage	74.7%	2.5%	8.0%	14.8%
	Total	448	15	48	89

Table 14. Prevalence of IMS

Question	Hospital Size	100% (all)	76-100%	51-75%	26-50%	1-25%	Not Applicable	Not Reported	p
What % of medical images in all other departments are managed by your IMS?	Large (>=400 beds)	12 (20.7%)	19 (32.8%)	9 (15.5%)	3 (5.2%)	7 (12.1%)	8 (13.8%)	42	p<0.001
	Medium (100-399 beds)	24 (14.8%)	46 (28.4%)	20 (12.3%)	11 (6.8%)	15 (9.3%)	46 (28.4%)	43
	Small (6-99 beds)	35 (12.3%)	40 (14.0%)	19 (6.7%)	21 (7.4%)	23 (8.1%)	147 (51.6%)	10
	Percentage	11.8%	17.5%	8.0%	5.8%	7.5%	33.5%	15.8%
	Total	71	105	48	35	45	201	95
What % of medical images in Radiology are managed by your IMS?	Large (>=400 beds)	29 (48.3%)	26 (43.3%)	2 (3.3%)	1 (1.7%)	0 (0.0%)	2 (3.3%)	40	p<0.001
	Medium (100-399 beds)	57 (34.8%)	89 (54.3%)	11 (6.7%)	1 (0.6%)	3 (1.8%)	3 (1.8%)	41
	Small (6-99 beds)	133 (45.5%)	71 (24.3%)	24 (8.2%)	10 (3.4%)	11 (3.8%)	43 (14.7%)	3
	Percentage	36.5%	31.0%	6.2%	2.0%	2.3%	8.0%	14.0%
	Total	219	186	37	12	14	48	84

This study shows that HIS and main ancillaries, such as laboratory, radiology, and pharmacy information systems, are present in all hospitals in Turkey. The availability of LIS, PAS, PACS, BI, and MIS varies between 63.6% and 94%. The prevalence of EHR functions such as CPOE, CDSS, clinical documents, and drug management also ranges from 70% to 99.5%. The size of the hospital has no significant relationship with the availability of those applications.

On the other hand, it is measured that not all hospitals having those applications adopted them. The proportion of hospitals using those applications and functions in at least half of the hospital varies between 57.3% and 79.3%. There is a significant relationship between the hospital size and the adoption of some indicators. For example, the adoption of clinical documents, medication, and non-medication orders is higher among small hospitals compared to larger hospitals. Similarly, the use of CDSS during drug orders and secondary verification of ordered medicines for inpatients is more frequently adopted by small hospitals.

On the other hand, Jha et al. (20) and DesRoches et al. (51) showed that the small hospitals in the USA are significantly slow to adopt EHR functions to larger hospitals. One reason may be extracted from the study of Thakkar and Davis, as their study concluded that the perception of the hospital managers in the USA is saying that the benefits of EHR adoption are more in larger hospitals concerning the smaller ones (52). Additional to this perception, the large hospitals in the USA can be suggested that they have high financial capabilities so that they can make investments to adopt EHR functions better than the smaller hospitals. However, Turkey, as a unitary state, is smaller than the USA, and the MoH can actively regulate all public hospitals. Thus the small hospitals in Turkey can be suggested that they can use their size as an advantage to change quicker than large hospitals.

When we consider the concepts of basic and comprehensive EHR functions as defined by Jha et al. (19) by comparing them with EMRAM stages (as expressed in Table 1), we found that 63.1% of all hospitals in Turkey have at least basic EHR functions, and 36% have comprehensive EHR functions. This result is better than the results of Korean hospitals of 2017 (26) but still lower than that of hospitals in the USA of 2015 (1) and 2017 (22), as depicted in Table 15.

Table 15. Comparison EHR adoption of the USA (in 2017), Korea (in 2017) and Turkey (in 2014-17)

Hospital Size	USA	Korea	Turkey
Basic EHR functions	41.4%	NA	27.1%
Comprehensive EHR functions	39.1%	NA	36%
Hospitals having at least basic EHR functions	80.5%	58.1%	63.1%

The adoption level of EHR functions in a country is very critical information since it can provide insights for healthcare policy establishment. Despite all the difficulties, the studies measuring overall adoption level of EHR are increasing. This study is the first one covering all public hospitals in a country by using EMRAM. As the first nation-wide study in Turkey, it is one of the studies like in the USA, Korea, and China, etc. which can enable the researchers to make benchmarking among countries. The results of this study are used by the MoH of Turkey to disseminate the benefits of EHR functions overall in the country.

As a limit of this study, there is no data about the adoption level of non-responder hospitals. The authors ignored that since a considerable amount of hospitals responded to the survey. For further studies, it may be interesting to focus on measuring the correlation of EHR adoption level and healthcare quality scores measured by international standards, such as Joint Commission International, etc.

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The datasets generated and analysed during the current study are not publicly available due the MoH regulations but are available from the corresponding author on reasonable request.

Competing interests

The authors declare that they have no competing interests

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Authors' contributions

IK generated the survey database and made relevant analysis, made literature review, and was a major contributor in writing the manusctript. JR helped in degining the survey and the quality control mechanism and contributed to writing the manuscript. SG enhanced the literature review and contructed the tables in the manuscript. SB, MMU, and IY planned the regional workshops of public hospitals, trained the hospital delegates and contributed to gathering the surveys from hospitals, and reviewed the manuscript.

Acknowledgements

The authors thank Dr. Korhan An for his valuable guidance for survey studies, and to Pakize Yigit for performing statistical analyses. The authors also thank collaboration groups, HIMSS Analytics Team, and MoH Team, for their valuable efforts for encouraging the hospitals to attend the survey studies and workshops and for contributions in the survey studies.

HIMSS Analytics Team: Suna Kirdag Mahir, Kubra Aycil, Beytiye O. Elmas, Esra Volkan, Zeynep Altinbas, and Gizem Gencyurek.

MoH Team: Esra Zehir, Berrin Gundogdu, Mert Ozcan, Ceyhan Vardar, Behcet Altinli, and Jale Sungur Hasancebi.

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The Adoption Level of Electronic Health Records in Turkish Hospitals and the Relation with Hospital Sizes

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Abstract

Figures

1 BACKGROUND

1.1. Overall Electronic Health Record Capabilities of Countries

1.2. Electronic Health Record Capability in Turkey

1.3. Electronic Medical Health Record Adaptation Model

2. Methods

3. RESULTS

3.1. Availability of Applications and Electronic Health Record Functions

3.1.1. Hospital Information Systems, Laboratory Information Systems, and Patient Administration Systems

3.1.2. Clinical Documents and Computerized Physician Order Entry

3.1.4. Medication Administration

3.1.5. Image Management

3.2. Usage of Applications and Electronic Health Record Functions

3.2.1. Clinical Documents and Computerized Physician Order Entry

3.2.2. Clinical Decision Support

3.2.3. Closed-Loop Medication Administration

3.2.4. Image Management

4. DISCUSSION

5. CONCLUSIONS

REFERENCES

DECLARATIONS

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Availability of data and materials

Competing interests

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Acknowledgements

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