Skip to main content

Table 2 Types of adoption curves and associated technologies

From: Implementation of innovative medical technologies in German inpatient care: patterns of utilization and evidence development

Type Criterion Technologies (no.)
(I) Continuous increase \(\frac{\mathrm{d}f(t)}{\mathrm{d}t}=: m\ (t)>0\)
t [2006; 2017]
HCI, MR-PTC, S-ICD, IAHEI, SCO-MAGN, BS-PV, SE-DES, TAVI, BS-VSAV, DCB-SUAV, DCB-LAV, DCB-AV, SCB-ULV, DCB-LLV, DCB-ARTV (n = 15)
(II) Continuous decrease \(\frac{\mathrm{d}f(t)}{\mathrm{d}t}=: m\ (t)<0\)
t [2006; 2017]
SE-BMS (n = 1)
(III) Reaching a saturation \(\frac{f(2015)}{f(2014)}<1.1\,\wedge\)
\(\frac{f(2016)}{f(2015)}<1.075\,\wedge\)
\(\frac{f(2017)}{f(2016)}<1.05\)
f(2015) > f(t)     t < 2015
EL-P/ICD, MRD, DCB-CV (n = 3)
(IV) “Local maximum”: Continuous increase followed by continuous decrease m(tI) > 0 
m(tII) < 0
tI [ti; tj], tII [tj; tk]
ti, tj < tk
FE-AAA, ACT, ACCS, DEB-TACE, LVRC, BVS, BRA, IABC, MVR, FD-IV (n = 10)
(V) “Local minimum”: Continuous decrease followed by continuous increase m(tI) < 0 
m(tII) > 0
tI [ti; tj], tII [tj; tk]
ti, tj < tk
MVAC, FD-ULV (n = 2)
(VI) Complex NA  
 (VI.a) According to hierarchical-agglomerative clustering method (see Additional file 2) PECLA/iLA, pVAD, EVCT, CBS, ACD, VEPTR, IAELC, UD-DJMS, DES-LLV, EABO, HCO, IAVC, SP-ENDOST, FDT, DES-SAA, MESI, EBV, NEURO, IABC-EL, DES-ULV, PVR, DCB-TV, DCB-OTHV (n = 23)
 (VI.b) According to hierarchical-agglomerative clustering method (see Additional file 2) IET-MICRO, F-TUR, DCB-IV, DCB-VV, ER-ABL (n = 5)