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Assessing the suitability of smart technology applications for e-health using a judgment-decomposition analytic hierarchy process approach

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Smart technologies have been extensively applied to e-health. However, whether existing smart technology applications for e-health are sustainable is questionable. To assess the sustainability of a smart technology application for e-health, this study proposes the judgment-decomposition analytic hierarchy process (JD-AHP) approach. In the proposed JD-AHP approach, a decision-maker’s judgment matrix is decomposed into several subjudgment matrices that are more consistent than the judgment matrix and that represent diversified viewpoints for assessing the sustainability of a smart technology application for e-health. Then, the priorities of factors critical to sustainability are derived from the subjudgment matrices by optimizing a mixed-integer nonlinear programming model. The JD-AHP approach was applied to assess the sustainabilities of 10 smart technology applications for e-health. According to the evaluation results, smart clothes had the least sustainability, whereas smart glasses and smart wheelchair had uncertain sustainabilities.

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Correspondence to Toly Chen.

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Appendix: The Enumeration Procedure

Appendix: The Enumeration Procedure

t1 = now;
A = [1 5 5 7 5;0.2 1 3 5 1;0.2 0.333 1 1 4; 0.143 0.2 1 1 3; 0.2 1 0.25 0.333 1];
[E V] = eig(A);
CI = (V(1,1)-5)/(5–1)
A1best = zeros(5,5);
A2best = zeros(5,5);
CI1best = 0;
CI2best = 0;
E1best = zeros(5,5);
E2best = zeros(5,5);
distbest = 0;
for i12 = 1:9
for i13 = 1:9
for i14 = 5:9
for i15 = 1:9
for i23 = 1:5
for i24 = 1:9
for i35 = 1:7
for i43 = 1:1
for i45 = 1:5
for i52 = 1:1
A1 = A;
A2 = A;
A1(1,2) = i12; A1(2,1) = 1/A1(1,2);
A1(1,3) = i13; A1(3,1) = 1/A1(1,3);
A1(1,4) = i14; A1(4,1) = 1/A1(1,4);
A1(1,5) = i15; A1(5,1) = 1/A1(1,5);
A1(2,3) = i23; A1(3,2) = 1/A1(2,3);
A1(2,4) = i24; A1(4,2) = 1/A1(2,4);
A1(3,5) = i35; A1(5,3) = 1/A1(3,5);
A1(4,3) = i43; A1(3,4) = 1/A1(4,3);
A1(4,5) = i45; A1(5,4) = 1/A1(4,5);
A1(5,2) = i52; A1(2,5) = 1/A1(5,2);
A2(1,2) = 2*A(1,2)-A1(1,2); A2(2,1) = 1/A2(1,2);
A2(1,3) = 2*A(1,3)-A1(1,3); A2(3,1) = 1/A2(1,3);
A2(1,4) = 2*A(1,4)-A1(1,4); A2(4,1) = 1/A2(1,4);
A2(1,5) = 2*A(1,5)-A1(1,5); A2(5,1) = 1/A2(1,5);
A2(2,3) = 2*A(2,3)-A1(2,3); A2(3,2) = 1/A2(2,3);
A2(2,4) = 2*A(2,4)-A1(2,4); A2(4,2) = 1/A2(2,4);
A2(3,5) = 2*A(3,5)-A1(3,5); A2(5,3) = 1/A2(3,5);
A2(4,3) = 2*A(4,3)-A1(4,3); A2(3,4) = 1/A2(4,3);
A2(4,5) = 2*A(4,5)-A1(4,5); A2(5,4) = 1/A2(4,5);
A2(5,2) = 2*A(5,2)-A1(5,2); A2(2,5) = 1/A2(5,2);
[E1,V1] = eig(A1);
[E2,V2] = eig(A2);
CI1 = (V1(1,1)-5)/(5–1);
CI2 = (V2(1,1)-5)/(5–1);
if CI1 < =CI & CI2 < =CI
dist = sqrt(sum(sum((A1-A2).^2)));
if dist>distbest
distbest = dist;
A1best = A1;
A2best = A2;
CI1best = CI1;
CI2best = CI2;
E1best = E1;
E2best = E2;
t2 = now;
runtime = (t2-t1)*24*60*60

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Chen, T., Wu, H. Assessing the suitability of smart technology applications for e-health using a judgment-decomposition analytic hierarchy process approach. Health Technol. (2020). https://doi.org/10.1007/s12553-020-00408-7

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  • Smart technology
  • e-health
  • Judgment decomposition
  • Analytic hierarchy process
  • Consistency