# Benefits of Advanced Air Mobility for Society and Environment: A Case Study of Ohio
## 4 Benefit Factors
### BF-6: Cost Savings in Agriculture
#### 4.7. BF-7: Life-Saving Value of Drones in Medical Delivery for Patients  
Medical delivery differs significantly from regular package delivery due to the delicate nature of items, namely, blood, lab samples, organs, medications, etc. As sUAS provides faster transmission of test samples, patients can receive treatment more quickly [31]. Here, the drones are considered suitable for organ delivery when the necessary configurations are applied. All medical deliveries are important without any doubt; however, emergency cases like Out of Hospital Cardiac Arrest (OHCA) should be given as the highest priority while designing the network of drones used for medical deliveries. When OHCA sufferers receive early defibrillation, they have a better chance of survival. In this paper, we focused on Automatic External Defibrillators (AEDs) delivery by drones in response to its critical scenario in the USA. According to the Ohio Department of Public Safety (ODPS), in the United States, Sudden Cardiac Arrest (SCA) is the leading cause of mortality [78]. They mentioned that OHCA accounts for around 250,000 deaths, and the yearly incidence of SCA in North America is 55 per 100,000 people. Using **P** (see Section 4.1 and Table 1), we calculated the number of OHCA sufferers (\(U_{OHCA_{(i)}}\)) in year \(i\) in SMCO:  
\[U_{OHCA_{(i)}}=\frac{55}{100,000}\times P_{i}\qquad i\in\{2022,2023,...,2032( year)\}. \tag{44}\]An optimized approach was done to deliver AED assisted drones to the OHCA patients, where the authors analyzed their results considering six cases of drone docking stations needed to build a drone-delivery network through a state [79]. In our report, we used their data of number of drone docking stations, and put those in **DSN**:  
\[DSN=[0,50,200,500,750,1015]. \tag{45}\]  
For the six cases of drone docking stations, we also took the data of percentage of survivors with respect to OHCA incidents (\(p_{s}\)) and cost per additional survivor relative to 'no drone' case (**CAS**):  
\[p_{s}=[12.3\%,12.9\%,13.3\%,13.8\%,14\%,14.4\%] \tag{46}\]  
\[CAS=[0,\$14752,\$31905,\$555792,\$73160,\$756495]. \tag{47}\]  
When the number of drones is zero, the percentage of survivors is the lowest. With the increase of drone docking stations through a state, the delivery time will be lower, resulting in more chances of saving more patients' lives. On the other hand, with the increase of drone docking stations, the cost of preparing the stations will also be increasing. In Equation (48), \(N_{S_{(ij)}}\) is a matrix of \(11\times 6\) order denoting the number of survivors in the next 11 years for the six drone docking cases:  
\[N_{S_{(ij)}}=U_{OHCA_{(i)}}\times p_{s_{(j)}}\qquad i\in\{2022,...,2032(year)\} ;j\in\{1,2,...,6\} \tag{48}\]  
Then, we computed \(N_{S_{(ik)}}\) using Equation (49) which stands for the number of increased survivors relative to the case of zero drones ('no drone' case or 1 st column of \(N_{S_{(ij)}}\)). Here, the order of \(N_{S_{(ik)}}\) is \(11\times 5\):  
\[N_{S_{(ik)}}=N_{S_{(ij)}}-N_{S_{(il)}}\qquad i\in\{2022,...,2032(year)\};j\in\{ 2,...,6\};k=j-1 \tag{49}\]  
\(CSS\) represents the cost savings per survivors that we evaluated using \(VSL_{i}\) (see Section 4.2 and Table 1) and \(CAS_{j}\):  
\[CSS_{(ik)}=VSL_{i}-CAS_{j}\qquad i\in\{2022,..,2032(year)\};j\in\{2,...,6\};k=j -1. \tag{50}\]  
\(TVSL\) is a matrix of \(11\times 5\) order denoting the total value of saving statistical life calculated using Equation (51):  
\[TVSL_{(ik)}=CSS_{(ik)\times N_{S_{(ik)}}}\qquad i\in\{2022,...,2032(year)\};k \in\{1,2,..,5\} \tag{51}\]