The impact of eHealth use on general practice workload in the pre-COVID-19 era: a systematic review

Table 2 Studies by MMAT categorization and their assigned effects on general practice workload

Study design according to MMAT categorization	Number of references^*	Number of effects assigned	+ ^a n (%)	-^b n (%)	0^c n (%)
Qualitative	81	109	72 (66)	31 (28)	6 (6)
Quantitative descriptive	56	73	35 (48)	27 (37)	11 (15)
Mixed method	44	67	30 (45)	26 (39)	11 (16)
Non-randomized	24	31	11 (35)	9 (29)	11 (35)
Randomized controlled trials	3	4	1 (25)	1 (25)	2 (50)
Total	208	284^d	149 (52)	94 (33)	41 (14)

^*The number of references is lower than the number of effects assigned because some studies reported more than one effect of eHealth service use on general practice workload (see the data analysis section)
^a Number of references reporting an increase in workload as a result of eHealth service use
^b Number of references reporting a reduction in workload as a result of eHealth service use
^c Number of references reporting no effect on workload as a result of eHealth service use
^d Because this analysis was focused on the MMAT-categorization and not on the type of eHealth service, this number is lower than the 304 effects assigned in Table 1

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