Artificial Intelligence (AI) Enabled Decision Support Tool for Selection of Patients for Lumbar Spine Surgery: a Feasibility Study
Phase: NA
Conditions: Intervertebral Disc Displacement, Spinal Stenosis Lumbar, Lumbar Disc Herniation, Lumbar Spine Degeneration
Sponsor: University Hospital of North Norway

Clinical Investigation Plan 
CIP-code:  
CIP-01.0 
1 
 
 
CLINICAL INVESTIGATION PLAN 
 
 
Artificial intelligence (AI) enabled decision support tool for selection of 
patients for lumbar spine surgery 
 
Version number:  
 
3.0  
 
Date:   
 
 
2024.11.05 
 
Sponsor:  
University Hospital of North Norway  
(Universitetssykehuset Nord-Norge HF) 
Hansine Hansens veg 67 
9019 TROMSØ 
 
Coordinating investigator  
Tor Ingebrigtsen  
Professor and consultant neurosurgeon 
University Hospital of North Norway, Tromsø, Norway 
Email: tor.ingebrigtsen@unn.no. 
 
 
 
 
 
 
 
 
Revision history 
Document version 
Date of Issue 
Summary of Change 
1.0 
2024.03.22 
 
1.1 
2024.05.03 
 
3.0 
2024.11.05 
Revision after feedback from 
REK KULMU, the monitor 
and UNN’s Data protection 
officer 
 
 

Clinical Investigation Plan 
CIP-code:  
CIP-01.0 
2 
 
Signatures 
 
 
Sponsors representative 
I am responsible for ensuring that this clinical investigation plan (CIP) includes all essential 
information to be able to conduct this clinical investigation. It is also my responsibility to 
appoint a dedicated independent monitor and approve the monitoring plan. 
 
I will submit the CIP and all other important investigation-related information to the 
responsible investigator(s) so that they can conduct the clinical correctly. I am aware that it is 
my responsibility to hold the staff members who work with this clinical investigation 
informed and trained. 
 
Sponsor’s signature 
 
 
 
 
 
 
 
__________________________________________________________________________ 
Tove Skjelbakken, director of research and education 
 
 
Date 
 
 
Coordinating investigator  
I have read this CIP and agree that it includes all essential information to be able to conduct 
the clinical investigation. By signing my name below, I agree to conduct the clinical 
investigation in compliance with this CIP, the Declaration of Helsinki, SS-EN ISO14155:2020 
(Good Clinical Practice), and the current national and international regulations governing the 
conduct of this clinical investigation. 
 
I will submit this CIP and all other important investigation-related information to the staff 
members and investigators who participate in this clinical investigation, so that they can 
conduct the clinical investigation correctly. I am aware of my responsibility to continuously 
keep the staff members and investigators informed and trained.  
 
I am aware that quality control of this clinical investigation will be performed in the form of 
monitoring, audit, and possibly inspection. 
 
Coordinating investigator’s signature 
 
 
 
 
 
 
_________________________________________________________________ 
Tor Ingebrigtsen, professor and consultant neurosurgeon  
 
Date 
 
 
 
 

Clinical Investigation Plan 
CIP-code:  
CIP-01.0 
3 
 
 
Principal investigators 
I have read this CIP and agree that it includes all essential information to be able to conduct 
the clinical investigation. By signing my name below, I agree to conduct the clinical 
investigation in compliance with this Clinical investigation plan, the Declaration of Helsinki, 
SS-EN ISO14155:2020 (Good Clinical Practice), and the current national and international 
regulations governing the conduct of this clinical investigation. 
 
I will submit this CIP and all other important clinical investigation-related information to the 
staff members who participate in this clinical investigation, so that they can conduct the 
clinical investigation correctly. I am aware of my responsibility to continuously keep the staff 
members who work with this clinical investigation informed and trained.  
 
I am aware that quality control of this clinical investigation will be performed in the form of 
monitoring, audit, and possibly inspection. 
 
Principal investigator’s signature 
 
 
___________________________________________________________________________ 
Tor Ingebrigtsen, professor and consultant neurosurgeon  
 
Date 
 
 
Principal investigator’s signature 
 
___________________________________________________________________________ 
Cato Kjærvik, associate professor and consultant orthopedic surgeon 
Date 
 
 
 
 

Clinical Investigation Plan 
CIP-code:  
CIP-01.0 
4 
 
Contact information 
Roles 
 
Sponsors representative 
Tove Skjelbakken 
Director of research and education 
University hospital of North Norway, 
Tromsø. 
tove.skjelbakken@unn.no 
 +47 930 48973 
Monitor 
Ann Helen Jakobsen 
Research monitor 
kliniskmonitor@unn.no 
+47 951 31862 
Coordinating and Principal investigator 
• 
Main responsibility for planning and 
conduction of the study, including 
analysis and publication 
• 
Responsible for recruitment and follow-
up of surgeons at the University Hospital 
of North Norway 
• 
Main supervisor for the ph.d.-candidate 
• 
Emergency contact 
Tor Ingebrigtsen  
Professor and consultant neurosurgeon 
University Hospital of North Norway, 
Tromsø 
tor.ingebrigtsen@unn.no 
+47 911 99843 
Sub-investigator 
• 
Co-responsibility for planning and 
conduction of the study, including 
analysis and publication 
• 
Co-supervisor for the ph.d.-candidate 
Tore Solberg 
Professor and consultant neurosurgeon, 
University Hospital of North Norway, 
Tromsø 
tore.solberg@unn.no  
+47 913 64531 
Sub-investigator 
• 
Co-responsibility for planning and 
conduction of the study, including 
analysis and publication 
• 
Co-supervisor for the ph.d.-candidate 
Karl Øyvind Mikalsen 
Associate professor and center director, 
The Norwegian center for clinical artificial 
intelligence, 
University Hospital of North Norway, 
Tromsø 
karl.oyvind.mikalsen@unn.no 
+47 915 65867 
Principal investigator 
• 
Responsibility for recruitment and 
follow-up of surgeons and conduction of 
the study at the Nordland Hospital 
Cato Kjærvik 
Associate professor and consultant 
orthopedic surgeon 
Nordland hospital, Bodø 
cato.kjaervik@nordlandssykehuset.no 

Clinical Investigation Plan 
CIP-code:  
CIP-01.0 
5 
 
• 
Participant in analysis and publication 
+47 416 62667 
Sub-investigator and ph.d.-candidate 
• 
Responsible for recruitment and follow-
up of patients at the University Hospital 
of North Norway 
• 
Responsible for analysis of data and 
publication (under supervision) 
Henrik Lykke Joakimsen 
hjo121@uit.no  
Phone +47 934 29542 
Sub-investigator 
• 
Responsible for recruitment and follow-
up of patients at the Nordland Hospital 
• 
Participant in analysis of data and 
publication 
Oliver Muller 
Consultant orthopedic surgeon  
omu001.Mueller@nordlandssykehuset.no 
+47 476 46183. 
IT-implementer 
• 
Participant (voluntary) in analysis of 
data and publication 
Department for e-health, integrated care and 
innovation 
University Hospital of North Norway, 
Tromsø 
Participants: 
Eli Larsen 
eli.larsen@unn.no  
IT-implementer 
• 
Participants (voluntary) in analysis of 
data and publication 
Northern Norway regional health authority 
clinical IT-implementation project 
organization 
Participants:  
Heidi Johansen 
Project manager  
Northern Norway Regional Health 
Authority, Tromsø 
heidi.johansen@helsenord.no  
Data processor and IT-developer  
• 
Participants (voluntary) in analysis of 
data and publication 
Northern Norway regional health authority 
ICT trust 
Participants:  
Arne Magnus Sandvik, product owner 
Torkil Grindstein, quality registry 
consultant 
Are Edvardsen, innovation consultant 
Northern Norway Regional Health 
Authority ICT Trust, Tromsø 
arne.magnus.sandvik@hn-ikt.no 
 

Clinical Investigation Plan 
CIP-code:  
CIP-01.0 
6 
 
Industry partner (manufacturer) 
• 
Participants (voluntary) in analysis of 
data and publication 
DIPS ASA 
Participants:  
Liv Bollvåg, research director 
Jørgen Aarmo Lund, ph.d.-student 
Thor Stenbæk, senior system architect  
Bjørn Næss, software developer 
lgb@dips.no  
 
Industry partner  
• 
Participant (voluntary) in analysis of 
data and publication 
Deepinsight AS 
Participants: Olav Willumsen Haugå, CEO 
Email: olav@deepinsight.no 
 
 
Funding and research agreement  
Funding for the studies is provided by the Northern Norway regional health authority 
(innovation grant), UiT The Arctic University of Norway (ph.d.-grant) and the Norwegian 
Medical Association (quality improvement grant). All project partners contribute in-kind. 
 
 
 

Clinical Investigation Plan 
CIP-code:  
CIP-01.0 
7 
 
List of acronyms and abbreviations 
 
Abbreviation 
Term/Explanation 
AE 
Adverse event 
AI 
Artificial intelligence 
CIP 
Clinical investigation plan 
AUC 
Area under the curve 
DST 
Decision support tool, interchangeably referred to as “device” 
EHR 
Electronic health record 
EQ-5D-5L 
EuroQol five-dimensions five-levels  
(measure of health-related quality of life) 
GPE 
Global perceived effect 
HN IKT 
Northern Norway regional health authority information technology trust  
(Helse Nord IKT) 
 
HN FRESK 
Northern Norway regional health authority clinical IT-implementation 
project organization 
(Helse Nord FRESK) 
 
IT 
Information technology 
LDH 
Lumbar disc herniation 
LSS 
Lumbar spinal stenosis 
MDR 
Medical devices regulation 
ML 
Machine learning 
NORspine 
The Norwegian registry for spine surgery 
NRS 
Numeric rating scale 

Clinical Investigation Plan 
CIP-code:  
CIP-01.0 
8 
 
ODI 
Oswestry disability index 
PASS 
Patient acceptable symptom state 
PROM 
Patient reported outcome measure 
REC 
Regional committee for medical and health sciences research ethics 
ROC 
Receiver operated characteristic 
SAE 
Serious adverse event 
SDM 
Shared decision making 
SPKI 
The Norwegian center for clinical artificial intelligence  
(Senter for pasientnær kunstig intelligens) 
 
UNN 
University hospital of North Norway 
UiT 
UiT The arctic university of Norway 
WP 
Work package 
 
 
 
 
 

Clinical Investigation Plan 
CIP-code:  
CIP-01.0 
9 
 
1. Synopsis 
 
English 
 
Background 
One third of patients operated for lumbar disc herniation (LDH) or spinal stenosis (LSS) 
do not achieve substantial improvement. Studies indicate that well informed shared 
decision making (SDM) can improve the selection to surgery, and thus the outcomes. 
Numerous algorithms for outcome prediction have therefore been developed, and some 
use artificial intelligence (AI). Most are trained on small datasets, few are accurate, all are 
stand-alone or web-based applications not integrated in the electronic health record 
(EHR), and none are implemented in routine clinical practice. 
 
The Norwegian registry for spine surgery (NORspine) comprises a cohort of more than 
69,000 cases. We have used AI to analyze the dataset and predict the outcome, and 
developed a decision support tool (DST) which is seamlessly integrated in the EHR DIPS 
Arena®. We intend to use the tool to inform the SDM between surgeons and patients 
about the indication for surgery (yes or no), to increase the proportion with a successful 
outcome.  
 
The aim of the studies described in this clinical investigation plan (CIP) is to assess the 
safety and feasibility of the DST for use in a subsequent main effectiveness study. 
 
The device 
The DST (the device) is an integrate compound of software-solutions. Baseline data are 
registered by patients and surgeons on questionnaires integrated in DIPS Arena®, and 
transferred to NORspine. The data are also transferred (de-identified) to the AI-enabled 
prediction algorithm which operates in a cloud-based model hosting service. The 
algorithm has been trained and validated on a dataset from NORspine. The area under the 
curve for prediction of the main outcome (Oswestry disability index after12 months) in 
receiver operating characteristic analysis is very high (0.85) for LDH and moderate (0.72) 
for LSS. The model host also calculates outcomes (proportions with substantial, slight, or 
no improvement, and worsening) for the 50 cases with baseline variables most similar to 
the present case (“patients-like-me”). Finally, the individual prediction and the outcomes 
for the “patients-like-me” are transferred back and displayed in the regular user interface 
of DIPS Arena® for use in the SDM.  
 
Clinical investigations 
This CIP describes a feasibility study and clinical pilot (proof of concept) study. Both use 
convergent qualitative and quantitative mixed methods. The comparator is decision 
making in routine clinical practice, without use of the DST. 
 

Clinical Investigation Plan 
CIP-code:  
CIP-01.0 
10 
 
The feasibility study will include 20 patients with magnetic resonance imaging confirmed 
LDH or LSS referred for evaluation of the indication for surgery, and six surgeons who 
do the evaluations. The study will iteratively redesign the user interface of the DST until 
it is considered safe and feasible for use in the pilot study.  
 
The pilot study will include 100 patients and nine surgeons at two study sites and 
evaluate whether the DST is safe and feasible for use in a possible subsequent main 
effectiveness study. It will also enable sample-size calculation for the main study. 
 
Monitoring and adverse events 
Possible foreseeable adverse events (AEs) are use of the DST on patients not fulfilling 
the inclusion criteria, erroneous or incomplete recording of baseline data, error in data 
transfer between the components, and misinterpretation of the prediction. Such AEs could 
misinform the SDM and cause unjustified recommendations about undergoing surgery or 
not. 
 
The CIP describes data management and protection, the informed consent process, 
measures taken to avoid AEs, and monitoring and reporting in detail. 
 
Registration and publication 
This clinical investigation will be registered on ClinicalTrials.gov before the start of 
recruitment, and results will be disseminated by publication in international peer-
reviewed open-access journals. 
 
Norsk 
Bakgrunn 
En tredel av pasienter som blir operert for prolaps eller spinal stenose i ryggen oppnår 
ingen vesentlig bedring. Studier indikerer at godt informert samvalg kan forbedre 
utvelgelsen til kirurgisk behandling, og dermed resultatene. Mange har derfor utviklet 
prediksjonsalgoritmer, og noen av disse bruker kunstig intelligens (KI). De fleste er trent 
på små datasett, få har tilstrekkelig nøyaktighet, alle er til bruk i frittstående systemer 
eller nettbaserte applikasjoner og ingen er integrert i den elektroniske pasientjournalen 
(EPJ) eller implementert i rutinemessig klinisk praksis. 
Nasjonalt kvalitetsregister for ryggkirurgi (NKR, eng. NORspine) har registrert en kohort 
på mer enn 69 000 ryggoperasjoner. Vi har brukt KI til å analysere datasettet og predikere 
operasjonsresultatet, og utviklet et beslutningsstøtteverktøy som er sømløst integrert i 
EPJ-systemet DIPS Arena®. Intensjonen er å bruke verktøyet til et bedre informert 
samvalg mellom kirurger og pasienter i vurderingen av operasjonsindikasjon (ja eller 
nei), for å øke andelen som får et godt operasjonsresultat. 

Clinical Investigation Plan 
CIP-code:  
CIP-01.0 
11 
 
Målet med studiene som beskrives i denne forskningsprotokollen (eng. Clinical 
investigation plan; CIP) er å undersøke om beslutningsstøtteverktøyet er trygt og 
anvendelig for bruk i en eventuell etterfølgende nasjonal effektstudie. 
Utstyret 
Beslutningsstøtteverktøyet (utstyret) er en sammensetning av mykvare-løsninger. 
Baselinedata blir registrert av pasienter og kirurger i digitale spørreskjemaer som er 
integrert i DIPS Arena®, og overført til NKR. Dataene blir også overført (avidentifisert) 
til en KI-basert prediksjonsalgoritme som driftes i en sky-basert modellvertstjeneste. 
Algoritmen er trent og validert på et datasett fra NKR. Arealet under kurven for 
prediksjon av hovedutfallsmålet (Oswestry disability index etter 12 måneder) i ROC-
analyser er svært høyt (0,85) for prolaps og moderat (0,72) for spinal stenose. 
Modellvertstjenesten beregner også utfall (andelene med stor bedring, litt bedring, ingen 
tydelig bedring og forverring) blant de 50 pasientene med baseline-variabler mest lik den 
aktuelle pasienten («pasienter-som-meg»). Til slutt blir den individuelle prediksjonen og 
utfallet for «pasienter-som-meg» overført tilbake til DIPS Arena® og vist som et 
skjermbilde i det vanlige brukergrensesnittet slik at resultatet kan brukes i samvalget. 
Kliniske studier 
Denne protokollen beskriver en gjennomførbarhetsstudie og en klinisk pilotstudie. Begge 
bruker integrert kvalitativ og kvantitativ metode. Vi skal sammenligne 
beslutningsprosesser gjennomført med bruk av beslutningsstøtteverktøyet med 
rutinemessig klinisk praksis. 
Gjennomførbarhetsstudien skal inkludere 20 pasienter som er henvist til vurdering med 
tanke på ryggkirurgi etter å ha fått påvist prolaps eller spinal stenose med 
magnettomografi-undersøkelse (MR), og seks kirurger som gjør slike vurderinger. I 
denne studien skal vi gjennomføre gjentatte forbedringer av verktøyets brukergrensesnitt 
til det vurderes som anvendelig i pilotstudien. 
Pilotstudien skal inkludere 100 pasienter og ni kirurger på to sykehus, og evaluere om 
verktøyet er trygt og anvendelig for utprøving i en eventuell etterfølgende effektstudie. 
Den vil også fremskaffe foreløpige data om utfall til styrkeberegning for effektstudien. 
Monitorering og uønskede hendelser 
Mulige uønskede hendelser er bruk av verktøyet på pasienter som ikke fyller 
inklusjonskriteriene, feilaktig eller mangelfull registrering av preoperative data, feil i 
dataoverføring mellom komponentene, og feiltolking av resultatet av prediksjonen. Slike 
feil kan gi et feilaktig beslutningsgrunnlag for samvalget, og medføre ubegrunnede 
anbefalinger om å gjennomgå eller avstå fra kirurgisk behandling. 

Clinical Investigation Plan 
CIP-code:  
CIP-01.0 
12 
 
Protokollen beskriver databehandling og beskyttelse av personopplysninger, 
samtykkeprosessen, tiltak for å unngå uønskede hendelser, og monitorering og 
rapportering av studien i detalj. 
Registrering og publisering 
Studiene vil bli registrert på ClinicalTrials.gov før vi starter inklusjon av pasienter, og 
resultatene vil bli publisert i internasjonale fagfelle-vurderte åpen-tilgang tidsskrifter. 
 
 
 
 
 

Clinical Investigation Plan 
CIP-code:  
CIP-01.0 
13 
 
2. Identification and description of the investigational device 
 
Background 
Low back pain (with or without leg pain) caused by degenerative spondylosis is a major 
cause of non-fatal health loss [1]. In Norway, it is the most common cause for short-term 
sick leave and the second most common for disability benefits [2].  
 
A Cochrane review found conflicting evidence on the effectiveness of surgical treatment 
[3], and only two thirds of the cases registered in the Norwegian registry for spine 
surgery (NORspine) report complete recovery or substantial improvement 12 months 
after the operation [4]. Lack of evidence-based guidelines and the fact that spine surgery 
is preference-sensitive contribute to variation in surgical rates, techniques and patients’ 
outcomes between hospitals, health regions and countries [5-7]. 
 
Studies show that a complex interplay between patient- and treatment-related factors 
influence the outcome [8]. It is believed that well informed shared decision-making 
(SDM) can improve selection to surgery, and thus the outcomes, but this requires accurate 
methods for outcome prediction [9, 10]. Numerous algorithms for outcome prediction in 
spine surgery have therefore been developed [9, 11-14], and some are AI-based [15-18]. 
Most are trained on small data sets, and few have reached a satisfactory accuracy with an 
area under the curve (AUC) >0.80 in receiver operating characteristic (ROC) analysis. A 
recent review from our group shows that all are stand-alone or web-based systems not 
integrated in the electronic health record (EHR), and none are implemented in routine 
clinical practice [19]. 
 
NORspine is a large national clinical quality registry which covers all public and private 
providers and has a capture rate of 80 % [20]. The cohort comprises more than 69,000 
cases (2023) and the dataset contains a comprehensive set of baseline-, process- and 
outcome variables recommended by the International Consortium for Health Outcome 
Measurement (ICHOM). The primary outcome is the Oswestry disability index (ODI), 
which is a disease-specific patient reported outcome measure (PROM) of functional 
disability [20]. NORspine thus provides a unique dataset for development of accurate AI-
based outcome prediction. 
 
We hypothesize that improved selection of patients who are likely to benefit from an 
operation will reduce the proportion of unsuccessful operations, and thus improve the 
outcomes after spine surgery. Recently, we explored use of the dataset in NORspine for 
AI-based prediction of success (complete recovery or substantial improvement) after 
operation for lumbar disk herniation, and achieved an AUC of 0.82 [21]. In parallel, we 
have developed a clinical decision-support tool (DST) for SDM which is seamlessly 
integrated in the regular user interface of the EHR-system DIPS Arena®. 
 

Clinical Investigation Plan 
CIP-code:  
CIP-01.0 
14 
 
Aims 
The overreaching aim of our innovation and research is to complete the development of 
and assess the safety, feasibility and effectiveness of an AI-enabled DST for lumbar disc 
herniation (LDH) and lumbar spinal stenosis (LSS).  
 
Function 
The DST is an AI-enabled algorithm which is trained on data in NORspine to predict the 
outcome of surgery for LDH and LSS. The tool is seamlessly integrated in the regular 
user interface of DIPS Arena®. We intend to use the tool to inform and support SDM 
about the indication for surgery (yes or no) between spine-surgeons and patients during 
outpatient clinic consultations. 
 
Details of development 
The development was initiated by NORspine, and organized as a project by the 
Norwegian center for clinical artificial intelligence (SPKI). NORspine and SPKI are 
hosted by the University hospital of North Norway (UNN). The project partnered with all 
other stakeholders at the UNN, the Machine-learning group at UiT the Arctic university 
of Norway, the Northern Norway regional health authority ICT trust (HN IKT) and 
clinical IT-implementation project organization (HN FRESK), the Research department at 
DIPS ASA and the health-data analyst company Deepinsight AS. 
 
We organized the development in six work-packages (WPs):  
 
The prediction algorithm 
Preliminary modelling verified that satisfactory accuracy (defined as an AUC > 0.80) for 
identifying successful outcomes (yes/no) can be achieved [21]. 
 
In WP1, we used a dataset of all recorded cases in NORspine operated from 01.01.2007 
through 10.06.2022 (n=69,672) to develop the prediction algorithm. We excluded cases 
with obviously erroneous values for age, body weight and height in logical checks 
(n=141) and cases with missing data for the primary outcome (ODI) at baseline (n=877) 
or at both 3- and 12-month follow-up (n=12,486). For cases who responded after 3, but 
not 12 months, we imputed the ODI reported at 3-months as a substitute for the missing 
data at 12 months (the last value carried forward-method). We excluded cases not 
operated for LDH or LSS (n=8,455) and cases who underwent an emergency operation 
for cauda equina syndrome (n=459).  
 
The dataset thus consisted of 47,254 cases operated for LDH (n=22,597) and LSS 
(n=24,657). We split the data in training/validation- (80%) and test-sets (20 %). 
 
During the algorithm development, numerous models were tested for both dichotomous 
outcomes (treatment success or non-success) and prediction of a continuous outcome 
(ODI) 12 months after the operation. The most accurate results were achieved with an 

Clinical Investigation Plan 
CIP-code:  
CIP-01.0 
15 
 
extreme gradient boosting (XGBoost) model for prediction of the continuous outcome. 
The model for LDH reaches a very high accuracy with AUC of 0.85, root mean squared 
error (RMSE) 14 and R2 0.27, while the model for LSS reaches a moderate accuracy 
(AUC 0.72, RMSE 15 and R2 0.29). 
 
The algorithm will not be changed during the feasibility and pilot study, but it can be re-
trained for future versions, to utilize the continuously increasing amount of data in the 
registry to improve the accuracy. The AI-based prediction will be used to inform the 
SDM about possible outcomes on the individual level.  
 
WP1 has also developed functionality for display of the outcomes for the 50 cases with 
baseline variables most similar to the present case (patients-like-me). Presentation of their 
outcomes will be used to inform the SDM about possible outcomes at the group level. 
The risk for indirect identification is mitigated by data minimization (e. g. collapsing 
continuous age into 5-year age categories) before data transfer to the model host, and 
anonymization is ensured by display only of proportions in the different outcome 
categories in DIPS Arena®. 
 
Software integrations 
WP2 digitalised NORspine’s questionnaires and integrated them in DIPS Arena®. WP3 
developed data transfer between DIPS Arena® and a cloud-based model hosting service 
for the algorithm, which operates within the Norwegian health network. WP4 will 
complete a design for presentation of the predictions in the user interface of DIPS 
Arena®. WP5 developed the necessary adaptions in NORspine for transfer of data from 
DIPS Arena®. It also developed application programming interfaces (API) for 
integrations between DIPS Arena® and NORspine, and between DIPS Arena® and the 
model hosting service.  
 
WP6 resolved legal issues and clarified that the design can be used for clinical research, 
provided the necessary approvals are granted. 
 
Figure 1 illustrates the design of the solution. 
 

Clinical Investigation Plan 
CIP-code:  
CIP-01.0 
16 
 
 
Figure 1. Design of an AI-enabled clinical decision support tool for spine surgery integrated in the EHR. 
Baseline data are registered by patients (online) and surgeons on questionnaires integrated in DIPS Arena® 
(1), and automatically transferred to NORspine (2). The data are also transferred (anonymized) to a 
prediction algorithm trained on the dataset in NORspine which operates in cloud-based model hosting 
service within the Norwegian health network (3). Finally, the prediction is transferred back to DIPS Arena® 
and displayed in the user interface (4). 
 
Identification of the device 
The device is version 1.0 of an integrate compound of software-solutions which has not 
received a commercial name yet. It is for the conduct of this clinical investigation entitled 
Artificial intelligence (AI) enabled decision support tool for selection of patients for 
lumbar spine surgery (short: DST for spine surgery; Norwegian: Beslutningsstøtte 
ryggkirurgi). Version logging and traceability will be according to DIPS ASA’s regular 
established routine for software updates in DIPS Arena®. 
 
Use 
The surgeons who participate in the studies will be made aware that the final decision 
about the indication for surgery is the responsibility of the surgeon. The intention is to 
inform the SDM and retain a trustful surgeon-patient relation, and not to establish 
computer-paternalism [22].  
 
The user of the DST must be a certified medical doctor and a specialist in neurosurgery or 
orthopedic surgery, or in training in one of these specialties. We will provide the surgeons 
a training session which explains the intention, limitations and possible risks of the tool. 
The limitations, e.g. that the DST is developed for use during outpatient clinic 
consultations, and not intended for use in emergency cases or in cases with a baseline 

Clinical Investigation Plan 
CIP-code:  
CIP-01.0 
17 
 
ODI ≤ 22 (LDH) or ≤ 14 (LSS) will be emphasized. The training session contains a 
demonstration of the use and provides hands-on training on a fictive case.  
 
3. Justification for the design of the clinical investigation 
The studies will use convergent mixed methods of qualitative and quantitative design.  
 
Feasibility study (study 1) 
The first study uses an iterative process to assess the safety and develop the feasibility of 
the DST for a subsequent clinical pilot study.  
 
Integration of AI-based tools in healthcare providers’ regular information technology (IT) 
infrastructure and clinical workflow with acceptable user-friendliness is prerequisite for 
adoption [23, 24], and user-interaction is important to assure acceptable usability and 
safety [25]. Knowledge about the feasibility is therefore necessary to determine whether 
evaluation of a tool in comprehensive clinical studies is justified [26]. The feasibility-
study design is thus appropriate as the first introduction of a new device into clinical 
testing after lab-simulated development, because it evaluates the initial use of the device, 
while maintaining low risk for patients. The iterative design ensures that relevant 
feedback from surgeons, patients and other staff involved in the workflow at the 
outpatient clinic, leads to improvement of the DST, its usability and the related workflow.  
 
Pilot study (study 2) 
The second study is a clinical pilot (proof of concept) study. 
 
Evaluation of a DST as safe and feasible in a one-center setting with a limited number of 
participating surgeons and patients is not sufficient to justify a large effectiveness-study, 
since the feasibility can vary between hospitals with different staffs and workflows. 
Further, estimates of effect sizes are necessary to evaluate whether a subsequent 
confirmatory main study is justified, and to enable sample size calculation [27].  
 
The pilot study-design is appropriate to achieve this. After the feasibility study, a final 
prototype of the DST and the related workflow will be completed. The clinical pilot study 
design is appropriate for testing whether this prototype is safe and feasible for scaling to 
other hospitals and use among more surgeons.  
 
4. Risks and clinical benefits of the investigational device and clinical 
investigation 
Clinical benefits 
Only 61 % of the cases registered in NORspine in 2021 reported complete recovery or 
substantial improvement 12 months after the operation. Among the remaining 39 %, 

Clinical Investigation Plan 
CIP-code:  
CIP-01.0 
18 
 
corresponding to about 2,000 patients per year (2022), 34 % reported little or no benefit, 
while a subgroup of 5 % reported worsening. Complications such as dural tear or wound 
infection occurred in 1-2 % and 5 %, respectively [4]. More serious complications, such 
as nerve root injury, transfusion- or reoperation-requiring bleeding, venous 
thromboembolism, respiratory or cardiac complications are rare (<1 %), and peri-
operative death is extremely rare (<0,01 %).  
 
In addition, there is substantial geographical variation both in the surgical rates, success 
rates and the occurrence of complications [4, 7].  
 
These findings are in accordance with results from other large clinical quality registers 
for spine surgery.  
 
Accordingly, improved selection of patients who are likely to benefit from an operation 
will reduce the high number of patients who undergo operations without achieving health 
gain, and prevent avoidable complications in this group. In addition, increased 
consistency in the selection can reduce unwarranted variation. Further, a reduced number 
of non-successful operations will free resources and treatment capacity, and potentially 
shorten waiting-times.  
 
Adverse device effects 
We do not expect serious adverse events (SAE) related to the DST since it is a non-
invasive device (a software) providing information which is used to support decisions 
about whether a surgical intervention should be performed or not (EU’s Medical device 
regulation (MDR) risk class IIb). 
 
We will, however, register all possible adverse events (AE) in accordance with the MDR. 
We define an AE as an erroneous risk estimation which misinforms the SDM, and causes 
a risk for unjustified recommendations about the intervention. If not corrected by the 
surgeon’s discretion, this can lead to unwarranted recommendations about undergoing an 
operation for patients who may not benefit, and contrary, to recommendations about not 
undergoing an operation for patients who might have benefited from such treatment. 
 
Risks associated with participation in the clinical investigation 
Data from NORspine and other large clinical quality registries show that surgeons have 
low accuracy in their selection of patients for surgery in current practice. Our preliminary 
analysis of the prediction model’s accuracy indicates that the algorithm will perform 
significantly better than the individual surgeon’s discretion [21]. We do not know how 
application of the prediction through the DST will influence the SDM and the final 
decisions about the indication for surgery. However, on group-level, we anticipate that 
participation in the study will improve the SDM and reduce the risk for undergoing 
unwarranted operations. 
 

Clinical Investigation Plan 
CIP-code:  
CIP-01.0 
19 
 
Nevertheless, for individual patients, a risk for unjustified recommendations remains.  
 
Undergoing an unwarranted operation exposes the patient to the risk for complications 
explained above, and causes delay in the administration of other and potentially more 
efficient treatments. 
 
Being advised against an operation that would have been efficient exposes the patient to a 
risk for prolonged duration of pain and disability, and worsening of neurological deficits 
such as muscle weakness, which carries a small risk for permanent disability. Prolonged 
sick-leave and more disability increases the risk for long term inability to work [28]. 
Assessment of these risks must consider that the main benefit of surgical versus 
conservative treatment is shortening of the duration of symptoms. After 1-3 years, there is 
no difference in outcomes between patients with LDH who undergo surgery and those 
who do not [29]. For patients with LSS, the natural history is less consistent [30].  
 
Patients who are advised against an operation will be informed that they have access to 
re-assessment if the symptoms worsen or persist.  
 
Possible interactions with concomitant medical treatments as considered under the 
risk analysis 
The device does not interact with other medical treatments or devices.  
 
Steps that will be taken to control or mitigate the risks 
User training for the surgeons will mitigate the risks that surgeons and patients trust the 
DST uncritically. In the feasibility study, an investigator will observe the consultations 
and interview both surgeons and patients to understand how the SDM is perceived and 
used. The tool and the related workflow will be improved iteratively when shortcomings 
are identified.  
 
Rationale for benefit-risk ratio 
Current practice exposes patients for a considerable risk (39 %) of undergoing an 
operation without achieving health gain. We consider it likely that participation will 
reduce this risk. Contrary, participation can increase the risk for being advised against an 
operation that would have been efficient. The size of this risk in current practice is 
probably considerable, but unknown because NORspine and other prospective registers 
do not track patients who are evaluated for surgery, but not operated.  
 
We consider the mitigating steps are adequate to reduce the risks to an acceptable level, 
and emphasize that patients who are advised against an operation have continuous access 
to re-assessment if the symptoms persist or worsen over time. Altogether, we consider the 
risk as very low and probably lower than undergoing assessment within regular current 
practice, due to the stringent systematic approach to assessment embedded in the study 
protocols.  

Clinical Investigation Plan 
CIP-code:  
CIP-01.0 
20 
 
5. Design of the clinical investigation 
Feasibility study (study 1)  
Artificial intelligence (AI) enabled decision support tool for selection of patients for 
lumbar spine surgery - a mixed methods feasibility study 
 
Objectives 
To evaluate and iteratively redesign  
1. An AI-enabled DST for lumbar spine surgery  
2. The related workflow in a spine surgery outpatient clinic 
until the tool and the workflow is considered safe and feasible for use in a pilot 
prospective observational clinical (proof of concept) study. 
 
Study design 
This is a feasibility study using convergent interventional mixed methods qualitative and 
quantitative design with embedding to iteratively assess and improve the DST and the 
related workflow.  
 
Primary outcomes 
The primary outcomes (assessed with qualitative methods) are: 
1. Surgeons’ acceptability of the DST for a clinical pilot study (yes/no) 
2. Patients’ acceptability of the DST for a clinical pilot study (yes/no) 
Timeframe 
Acceptability will be assessed continuously, but finally evaluated towards the end of the 
study, after iterative redesign of the DST and the related workflow according to 
requirements identified with qualitative methods. 
 
Secondary outcomes 
The secondary outcomes (assessed with quantitative methods) are:  
1. Surgeons’ compliance rate  
(the proportion of consultations in which the surgeon uses the DST as intended) 
2. Patients’ compliance rate 
(the proportion of patients who complete the online questionnaire with the 
required information before the outpatient clinic visit) 
3. Duration of the consultation (minutes)  
 
Timeframe 
The rates and the duration will be calculated as averages for the study period, and 
towards the end of the study, after iterative redesign according to requirements identified 
with the qualitative methods. 

Clinical Investigation Plan 
CIP-code:  
CIP-01.0 
21 
 
 
Outcome assessment  
The qualitative data will be analyzed continuously and discretionary to identify 
requirements for iterative redesign of the DST and the related workflow, and to inform 
our understanding of the context (workflow, roles, workshare between surgeons and 
administrative staff, surgeon-patient relation, stakeholders trust in the intervention). The 
data from both methods will be connected throughout the development in the iterative 
process. The results will be presented by joint display to illustrate the feasibility of the 
DST, as described by Fetters et al. [31]. 
 
Follow-up 
In this study, we do not plan study-specific follow-up of patients or surgeons after the 
outpatient clinic consultation. Patients elected for surgery will be followed up according 
to the UNN’s regular clinical routine for lumbar spine surgery, which includes monitoring 
of PROMs according to the NORspine-protocol. Patients not elected for surgery will be 
referred back to their general practitioner for regular follow-up in primary care.  
 
Data collection 
 
Qualitative data 
The qualitative data will be collected from: 
1. Semi-structured interviews with surgeons, focusing on how they trust the 
prediction of the outcome, how the prediction influences the decision about the 
indication for surgery, how they perceive the DST’s and the related workflow’s 
safety and usability, and how the use influences time management during the 
consultation.  
2. Semi-structured interviews with patients, focusing on the online form’s usability, 
how they perceive and trust the DST, the SDM and the decision. 
3. Investigators’ observation of outpatient consultations with and without use of the 
DST 
4. Unstructured feedback from involved and non-involved surgeons and other staff 
during morning conferences, in the outpatient clinic and in meetings with the 
software developers. 
The semi-structured interviews will evolve around 6 to 12 open questions prepared in 
advance [32]. The interview guide will not exclude further comments from surgeons or 
patients, or supplemental questions from the investigator.  
 
Quantitative data 
Quantitative data on compliance rates and the duration of consultations will be collected 
by the investigators through participative observation. We will also record whether the 

Clinical Investigation Plan 
CIP-code:  
CIP-01.0 
22 
 
surgeon agrees with the prediction or not, and whether the outcome of the SDM 
(operation yes/no) agrees with the prediction of outcome categories.  
 
The iterative process 
User feedback will be given in meetings between the investigators and the software 
developers, and if necessary, the surgeons and the leadership of the outpatient clinic. We 
have not prespecified the frequency because it will depend on how the DST performs and 
integrates with the workflow. Resistance against changes in roles and the workflow is 
anticipated, and leadership involvement expected as necessary to overcome such 
problems.  
 
Study site/location 
University Hospital of North Norway, Tromsø, Norway, Sykehusvegen 38, 9019 Tromsø. 
 
Subjects 
 
Inclusion criteria 
• Patients with MRI-confirmed LDH or LSS referred to UNN Tromsø for 
evaluation of the indication for surgery 
• Specialists and physicians in training (for two years or more) in neurosurgery who 
evaluate such patients at the neurosurgical outpatient clinic 
 
Exclusion criteria 
• Patients unable to consent because of 
o Age < 18 years 
o Serious drug abuse of severe psychiatric disorders 
o Language barriers (patients who cannot speak or read Norwegian) 
• Patients with a baseline ODI ≤14 (LDH) or ≤22 (LSS) 
• Patients undergoing non-elective/emergency operations 
• Patients with degenerative conditions other that LDH and LSS, fractures, primary 
infections, or malignant conditions of the spine 
• Physicians in training with less than two years’ experience with spine surgery 
 
Recruitment and consent 
We will recruit approximately 20 consecutive patients referred to the neurosurgical 
outpatient clinic. Eligible patients will be identified by the investigators by continuous 
screening of the list of patients who have been accepted for a consultation, but not 
allocated an appointment. Assessment of eligibility will be based on information in the 
referral.  
 

Clinical Investigation Plan 
CIP-code:  
CIP-01.0 
23 
 
Eligible patients will receive a postal letter with information about the study, and an 
invitation to participate and return a signed written consent in a pre-stamped envelope. 
Patients will be informed that not participating will not influence their treatment. They 
will be provided a direct telephone number for contact with a sub-investigator if they 
want additional information or want to ask questions. Patients who do not respond within 
one week, will be contacted per telephone by a sub-investigator to clarify whether they 
want to consent or not, before their appointment is scheduled. Those who consent will be 
informed electronically about the scheduling of their appointment, and invited to provide 
their baseline data as described below, under Procedures. 
 
The six neurosurgeons will be recruited among the staff at the neurosurgical department 
by purposeful sampling done by the PI [33]. Initially, during the steepest learning curve 
of the iterative process, we will include two specialists and one physician in training who 
have participated in development of the DST and the study design. This follows the 
method of purposeful sampling, aiming for saturation or adequate information power 
[34]. When these surgeons begin to express satisfaction with the usability, we will recruit 
another two specialists and one physician in training who have not been previously 
involved. The surgeons must provide written informed consent before participation. 
 
Completion of the clinical investigation 
The study is complete when information saturation is reached, which we consider likely 
after inclusion of 20 patients and 6 surgeons. This is in accordance with recommendations 
of including a sample size of 6-12 to reach saturation in implementation research [35, 
36]. This estimate can be influenced by the number and character of design- and 
workflow-iterations, implying that saturation can be reached earlier or later. The study 
will be continued until the DST and the workflow is considered feasible for the pilot 
study, or until it is considered not feasible for continued evaluation, based on assessments 
of the primary and secondary outcomes.  
 
Time perspective 
Scheduled study period: 01.12 – 31.04.2025. 
 
Pilot study (study two) 
Artificial intelligence (AI) enabled decision support tool for selection of patients for 
lumbar spine surgery - a mixed methods pilot study 
 
Objectives 
1. To evaluate the safety and feasibility of the DST, the related workflow and the 
study design for use in a subsequent main (effectiveness) study 
2. To estimate the DST’s effectiveness to enable sample-size calculations for a 
subsequent main study 
3. To reach a decision about whether we should proceed with the main study or not 

Clinical Investigation Plan 
CIP-code:  
CIP-01.0 
24 
 
 
Study design 
This is a prospective observational clinical pilot study using a mixed method design to 
evaluate whether the DST and the study design has the safety and feasibility needed to 
proceed with a subsequent larger multicenter main effect study, and to enable sample-size 
calculations for the main study. We increase the number of participating hospitals from 
one to two to gain experience with transferability of the DST, the workflow and the study 
design. The preliminary estimations of effectiveness will be done by comparing 
participant’s outcomes 12 months after the operation with outcomes among cases 
undergoing routine preoperative evaluation, with use of NORspine as data source.  
 
Primary outcomes 
The primary outcomes (assessed with qualitative and quantitative methods) are:  
1. Surgeons’ acceptability of the DST and the related workflow for use in a larger 
multicenter main effect study (yes/no) 
2. ODI raw- and change-scores 12 months after the operation compared between 
participants and non-participants 
The sample-size calculation will be based on the ODI, and the evaluation of the study 
design on the surgeon’s assessments of the acceptability. The final evaluation of whether 
the subsequent main effect study is justified (yes/no) will be based on an integral 
assessment of evaluation of the safety, the acceptability and the estimate of the sample-
size required to show clinically relevant differences in effectiveness.  
 
Secondary outcomes 
The secondary outcomes will be assessed quantitatively 12 months after the operation:  
1. Numeric rating scale (NRS) raw- and change-scores for back- and leg-pain 
2. General perceived effect (GPE) scale score 
3. Work status  
4. EuroQoL five-dimension three-level questionnaire raw- and change-score 
 
Outcome assessment 
The qualitative data will be analyzed continuously and discretionary to assess the 
acceptability of the DST and the workflow after the surgeons have gained some 
experience with the tool. Results will be presented by joint display.  
 
Follow-up 
In this study, we do not plan study-specific follow-up of patients or surgeons after the 
outpatient clinic consultation. Patients elected for surgery will be followed up according 
to the hospitals’ regular clinical routine for patients undergoing lumbar spine surgery, 

Clinical Investigation Plan 
CIP-code:  
CIP-01.0 
25 
 
while patients not elected for surgery will be referred back to their general practitioner for 
regular follow-up in primary care.  
 
Patients who consent to registration in NOR-spine will be followed-up according to the 
registry’s regular routine with questionnaires after 3 and 12 months. Patients who 
withdraw their consent to participation in the study will be excluded from the retrieval of 
outcome data from NORspine, and notified that they must withdraw their consent to 
registration in NORspine separately, if they want to do so.  
 
Data collection 
 
Qualitative data 
The qualitative data will be collected from: 
1. Semi-structured interviews with surgeons, focusing on how the surgeons trust the 
prediction of the outcome, how the prediction influences the decision about the 
indication for surgery, how they perceive the DST’s and the related workflow’s 
usability, and how the use influences time management. 
2. Unstructured feedback from involved and non-involved surgeons and other staff 
during morning conferences and in the outpatient clinic. 
The semi-structured interviews will evolve around 6 to 12 open questions prepared in 
advance [32]. The interview guide will not exclude further comments from surgeons or 
patients, or supplemental questions from the investigator.  
 
Quantitative data  
Use of the DST does not require that the patient consents to participating in NORspine 
because the algorithm allows calculation of the prediction based on the patient’s baseline 
data, without data-exchange with NORspine. We assume, however, that most participants 
in this study will consent to registration in NORspine, since the capture rate was 80 % in 
2022 [20]. Therefore, NORspine can be used as a data source for between groups 
comparisons of outcomes among participants versus non-participants. Patients report the 
ODI and all secondary outcomes in this study to NORspine, and we will apply for access 
to these data.  
 
We will assess how much the patient and the surgeon perceived that the prediction 
influenced the SDM (on scales ranging from no to decisive influence). We will also 
record whether the surgeon agreed with the prediction (on a balanced Likert scale ranging 
from disagree completely to agree completely), and whether the surgeon considers that 
the decision (operation yes/no) would have been different (yes/no/uncertain) if the DST 
had not been used. 
 
 

Clinical Investigation Plan 
CIP-code:  
CIP-01.0 
26 
 
Study sites/locations 
 
1. University Hospital of North Norway Tromsø, Sykehusvegen 38, 9019 Tromsø, 
Norway 
2. Nordland Hospital Bodø, Parkvegen 95, 8092 Bodø, Norway 
 
Subjects  
 
Inclusion criteria 
• Patients with MRI-confirmed LDH or LSS referred to UNN Tromsø or Nordland 
Hospital Bodø for evaluation of the indication for surgery 
• Specialists and physicians in training (for two years or more) in neurosurgery or 
orthopedic surgery who evaluate such patients at the neurosurgical outpatient 
clinic at UNN Tromsø or the orthopedic outpatient clinic at Nordland Hospital 
Bodø 
 
Exclusion criteria 
• Patients unable to consent because of 
o Age < 18 years 
o Serious drug abuse of severe psychiatric disorders 
o Language barriers (patients who cannot speak or read Norwegian) 
• Patients with a baseline ODI ≤14 (LDH) or ≤22 (LSS) 
• Patients undergoing non-elective/emergency operations 
• Patients with degenerative conditions other that LDH and LSS, fractures, primary 
infections, or malignant conditions of the spine 
• Physicians in training with less than two years’ experience with spine surgery 
 
Recruitment and consent 
This study will recruit approximately 100 consecutive patients referred to the 
participating hospitals. Considering the larger sample size and previous studies from 
NORspine, we do not expect skewed inclusion, and do not plan for strategic inclusion. 
 
The planned enrollment is six neurosurgeons from UNN Tromsø and three orthopedic 
surgeons from Nordland Hospital Bodø. They will be recruited by the local PIs in Tromsø 
and Bodø, respectively. The neurosurgeons can overlap partially or completely with those 
participating in the feasibility study.  
 
The recruitment will otherwise follow the same procedures as in the feasibility study. 
 

Clinical Investigation Plan 
CIP-code:  
CIP-01.0 
27 
 
Written informed consent will be obtained from patients and surgeons as in the feasibility 
study. Patients who consent to registration in NORspine also consent to participation in 
research, such as the current study. This allows us to collect outcome data from 
NORspine and link to the data collected specifically for this study. The consent also 
covers use of data from patients who are registered in NORspine, but not participating in 
the present study as controls.  
 
Completion of the clinical investigation 
The study will be complete when 100 patients and nine surgeons are included, or by 30. 
April 2026, whichever is reached first. The exact number is not important, but it should 
be large enough to enable sample size calculation for the subsequent main study, which 
by empirical estimates require 50-150 patients, depending on the effect sizes of interest.  
 
Time perspective 
Scheduled study period: 31.04.2025 – 30.06.2027 
 
Measures to minimize bias 
 
Feasibility study (study one) 
This study uses a convergent interventional mixed methods qualitative and quantitative 
design, with weighting towards the qualitative methods, since assessment of the main 
outcomes is by semi-structured interviews.  
 
Researcher bias in qualitative research include selection bias, confirmation bias, 
interpretation bias, reporting bias and dissemination bias [37].  
 
Referrals from general practitioners (GPs) will be assessed by consultant surgeons 
according to routine practice. The list of patients who have been accepted for a 
consultation (but not allocated an appointment) will be screened continuously by the 
ph.d.-candidate, and eligible patients will be identified based on the information in the 
referrals. Eligible patients will be included consecutively to avoid selection bias. If the 
recruited patients show little variation in education, sex, age and ethnicity, we will 
consider strategic sampling among consecutively referred patients to increase 
representativity. 
 
We use reflexivity (keeping a reflective diary and peer debriefing with a researcher not 
involved in the study) and triangulation to mitigate confirmation, interpretation and 
reporting bias. The triangulation method is use of multiple qualitative data sources 
(observation, interviews and feedback from e.g. morning conferences) and collection of 
supplemental quantitative data. Dissemination bias is mitigated by publishing the 
protocol on ClinicalTrials.gov and by following the publication policy outlined in chapter 
15 in this CIP.  
 

Clinical Investigation Plan 
CIP-code:  
CIP-01.0 
28 
 
Pilot study (study two) 
This study also uses convergent interventional mixed methods qualitative and 
quantitative design, but the design is more balanced since one main outcome (surgeons’ 
acceptability) is assessed qualitatively, while the other (effectiveness) is assessed 
quantitatively by utilizing outcome data in NORspine. 
 
The measures to mitigate minimize bias in the qualitative part are as in study one, except 
that recruitment will be strictly consecutive to avoid selection bias. We assume that the 
larger sample size (n=100) will ensure representativity. NORspine collects the outcome 
data independently of the researchers participating in the study. This mitigates reporting 
bias. A pilot study is explorative, and use of blinding or randomization is therefore not 
appropriate. Estimating effectiveness is not an aim, so steps to reduce bias of effect-
estimates, such as randomization, serve no purpose. Patient who are operated during the 
same period at hospitals and registered in NORspine are controls. Propensity score 
matching will be done to ensure comparability. This method controls for hospital 
characteristics as a possible confounder. 
 
Procedures 
Table 1 (version 2, attached) describes the clinical procedures and workflow in the 
studies, including the differences between routine practice and the studies, in detail. 
 
Routine clinical practice 
In current routine practice, GPs refer patients with suspected LDH and LSS to MRI if 
they consider surgical treatment an option, based on the patient’s history (including the 
severity and duration of symptoms) and the clinical findings. If MRI confirms one of the 
diagnoses, they refer the patient to a neurosurgical or orthopedic outpatient clinic for 
assessment of the indication for surgical treatment by a spine surgeon. Patients who are 
accepted for a consultation receive a short message service (SMS) notification about the 
scheduled appointment to their mobile phone. 
 
At the outpatient clinic, a consultation which includes reviewing the patient’s medical 
history, a physical examination and re-assessment of the MRI findings is done by the 
spine surgeon. The surgeon then evaluates the possible outcome after surgery by doing an 
integral discretionary assessment, and completes the SDM with the patient to reach a 
final conclusion about the indication for surgery (yes or no). 
 
In this routine, patients who are selected for surgical treatment are invited to participate in 
NORspine when they are admitted for the operation. Patients and surgeons report 
baseline data to NORspine on paper questionnaires, but these data are not used in the pre-
operative decision making. 
 
Patients who consent to registration in NORspine will are invited to report patient 
reported outcome measures (PROMs) after 3 and 12 months by completing an electronic 

Clinical Investigation Plan 
CIP-code:  
CIP-01.0 
29 
 
questionnaire online. Patients unable to respond electronically are provided a paper 
questionnaire by regular mail.  
 
Feasibility study (study one) 
In this study, included patients will be invited to fill out an electronic version of 
NORspine’s preoperative questionnaire online, before the consultation at the outpatient 
clinic. A SMS notification will include a link to NORspine’s baseline questionnaire. 
Access requires logon to www.helsenorge.no. This is a pilot for a general service being 
developed by the National health network for collection of structured data from patients 
to the EHR.  
 
At the beginning of the consultation, a summary of the information provided by the 
patient will be presented to the surgeon in the regular user-interface of DIPS Arena®, as a 
basis for the conversation. Next, the surgeon completes the preoperative part of 
NORspine’s questionnaire for registration of medical information, and requests the 
prediction. Finally, the DST computes the outcome prediction and displays it in the user-
interface of DIPS® Arena, and the information will be used to inform the SDM between 
the surgeon and the patient. Patients who are selected for surgery will receive written 
information about NORspine, and be invited to consent to participation, according to the 
established routine, before transfer of the data to the registry. After the consultation, the 
patient will be asked to participate in a semi-structured interview with the investigator.  
 
Patients who consent to registration in NORspine will be invited to fill out the 
questionnaire again within the last two weeks before the operation, if the waiting time 
exceeds two weeks. 
 
The subsequent process before undergoing surgery (or no surgery), the surgical 
procedures and the follow-up from NORspine does not deviate from routine clinical 
practice.  
 
Pilot study (study two)  
The procedure will be similar in the pilot study, except there is no interview with the 
patients after the consultation.  
 
Compromising factors 
A concern for the feasibility of both studies, is patients’ ability to access and complete the 
questionnaire online before the consultation. Based on experience during the feasibility 
study, we will consider the need for assistance with completion of the questionnaire 
online or on paper (followed by punching of the data) prior to the consultation with the 
surgeon.  
 
 
 

Clinical Investigation Plan 
CIP-code:  
CIP-01.0 
30 
 
Investigational device and comparators 
The AI-enabled DST integrated in DIPS Arena® is the only investigational device to be 
evaluated. We do not plan to change the input data or the ML algorithm during the study 
period. The user-interface of the DST and the related workflow will be iteratively 
redesigned during the feasibility study, and we will allow minor improvements also 
during the pilot study, if deemed necessary for transferability to the second study site. 
 
The comparator is decision making in current routine clinical practice, without use of 
decision support tools.  
 
Monitoring plan 
The Research department at the UNN will be responsible for the monitoring on behalf of 
the sponsor. The sponsor has dedicated a monitor who will be responsible for overseeing 
the progress of the investigation and to verify that it is conducted, recorded, and reported 
in accordance with this CIP and subsequent amendments, written procedures NS-EN ISO 
14155, and other applicable regulatory requirements. The monitoring will be conducted 
according to a written monitoring plan, which will be agreed when the study has been 
approved by the Norwegian medical products agency and the Ethics committee for 
clinical investigation of medicines and medical devices (REK KULMU). 
 
6. Statistical considerations 
Feasibility study (study one) 
Descriptive statistics about the patients and surgeons, their acceptability of and 
compliance with the DST, the degree to which the surgeon agrees with the prediction or 
not, and the proportion in which the outcome of the SDM (operation yes/no) agrees with 
the prediction (success or failure) of the outcome, and the duration of the consultations 
will be calculated. Missing data will be reported. Imputation will not be done. Further 
analysis will not be required.  
 
Pilot study (study two) 
Analysis population: Participants who receive use of the DST in the SDM about the 
indication for surgery and subsequently undergo the operation (study group) will be 
compared to propensity score-matched cases registered in NORspine who were operated 
during the same time period without use of the DST (control group). Descriptive statistics 
of baseline data, the operation, complications and PROMs 3 and 12 months post-
operatively will be calculated. Missing data will be reported, but imputation will not be 
done. The results will provide rough estimates of variability, effect size and correlation, 
which is necessary for sample size calculation for the subsequent main study. 
Significance will be defined to 0.05-level.  
 
 

Clinical Investigation Plan 
CIP-code:  
CIP-01.0 
31 
 
7. Data management and protection 
The qualitative and quantitative data collected specifically for this study through 
interviews and participatory observation will be recorded electronically and stored de-
identified in a project specific and password protected folder on a secure research server 
at the UNN, approved by the data protection officer (DPO). The identificatory key will be 
stored in a separate folder. Access is granted by the DPO and restricted to the 
investigators. The qualitative data will be saved in raw text format and the quantitative 
data in UNN’s research data sampling system REDCap. Routines for this data 
management are well established, the complexity and amount of data is limited and there 
will be no data exchange with or export to other organizations.  
 
In this project, the baseline data that are routinely collected on paper questionnaires from 
patients who consent to registration in NORspine, will instead be collected electronically 
and stored in DIPS Arena® at the UNN and the Nordland Hospital as regular EHR-
content. This data storage adheres to the established regulatory requirements for EHRs. 
The UNN and the Nordland Hospital store their data separately and in accordance with 
DPIAs for their EHRs. 
 
The data-transfer from DIPS Arena® to NORspine and between DIPS Arena® and the 
cloud-based model hosting service which operates the algorithm, uses APIs developed by 
HN IKT. This adheres to established regulatory requirements for data transfer between 
hospital enterprises and the national clinical quality registers hosted by HN IKT. The 
algorithm uses anonymized data. All patient identifiable data are thus stored in DIPS 
Arena® and NORspine. 
 
Study participants who consent to registration in NORspine, will have their baseline data 
automatically copied and transferred from DIPS Arena® to NORspine.  
 
In the pilot study (study 2), baseline- and outcome-data will be retrieved from NORspine, 
stored on UNN’s secure research server as described above, and linked with the data 
collected specifically for the studies by use of the identificatory key. All analyses will 
then be done on de-identified data within the same secure environment as described 
above.  
 
NORspine is hosted by the UNN and the CEO is the formal data controller. NORspine’s 
data management is in accordance with a DPIA for the registry approved by the DPO. 
HN IKT is data processor on behalf of the UNN, according to a data processing 
agreement.  
 
 
 

Clinical Investigation Plan 
CIP-code:  
CIP-01.0 
32 
 
8. Amendments to the CIP 
The Norwegian medical products agency and the REC shall be notified of all proposed 
changes to the approved CIP that are likely to have a substantial impact on the safety, 
health or rights of the study participants or on the robustness or reliability of the clinical 
data generated by the investigation, as required in Article 75 of the MDR. The 
coordinating investigator must wait for 38 days before any modification is implemented 
(unless any of the exceptions in Article 75 applies) or for approval of the modifications, 
whichever comes first, before implementing the changes. A CIP with approved 
substantial modifications will be filed as a new main version (e.g. CIP code change from 
01 to 02). 
 
Non-substantial amendments to the CIP can be suggested by the PI and approved by the 
sponsor. Changes cannot be implemented without approval from the sponsor. A non-
substantial modification will be filed as a new sub-version (e.g. CIP code change from 
01.0 to 01.1). 
 
It is the coordinating investigator’s responsibility to inform all PIs and sub-investigators 
about amendments to the CIP, and to oversee consistent implementation of approved 
changes. 
 
9. Deviations from the CIP 
The investigators are not allowed to deviate from the CIP except to protect the rights, 
safety, and well-being of human subjects under emergency circumstances, when the 
investigator may deviate without prior approval from the sponsor. Waivers from the CIP 
are not permitted. 
 
Sub-investigators and PIs must report deviations from the CIP to the coordinating 
investigator within one day, and the coordinating investigator must notify the sponsor on 
the first subsequent work day. The PIs must record all such deviations in the hospitals’ 
electronic reporting system (Docmap) as a non-medical adverse event (AE) within one 
work day after their occurrence (the two study sites use similar systems for recording and 
analyses of AEs). A simplified event analysis according to the hospitals’ regular routines 
will be done within two weeks. The coordinating investigator is responsible for the 
analyses. 
 
In the case of unwarranted deviations from the CIP, the coordinating investigator is 
responsible for notifying and correcting sub-investigators. Repeated unwarranted 
deviations may lead to disqualification of sub-investigators. 
 
 

Clinical Investigation Plan 
CIP-code:  
CIP-01.0 
33 
 
10. Device traceability and accountability 
Use of the DST will be limited to the principal investigators, the clinical sub-investigators 
and the surgeons who participate in the study by user access control, i.e. access to the tool 
will only be available for specific investigators and surgeons logged on the hospitals’ IT-
system and the DIPS Arena® EHR (two steps) with their personal user ID and password. 
These users will be given access to open and register data in the documents stored in 
DIPS Arena® and access to the prediction presented by the DST. 
 
The DST will be labeled «Exclusively for clinical investigation» in the user-interface.  
The sponsor provides the study sites with a written instruction for use (IFU) and technical 
support. The principal investigators shall keep records documenting names of the persons 
who have the DST available on their EHR-account, who use it during the study, dates of 
use, which version they use, and subject (patient) identification.  
 
HN IKT will keep a log specifying which users have access at any time. They will also 
log lookups made by users of the documents.  
 
11. Statements of compliance 
This clinical investigation will be conducted in compliance with the MDR. It also 
complies with current national and international regulations governing clinical 
investigations, the ethical principles that have their origin in of the Declaration of 
Helsinki [38] and the standard ISO 14155:2020 Clinical investigation of medical devices 
for human subjects – Good clinical practice. 
 
The clinical investigation will not begin until the required regulatory and ethical 
assessments have been completed with non-negative outcomes, in accordance with the 
MDR and national legislation. Any additional requirements imposed by the REK or a 
regulatory authority will be followed, if appropriate. 
 
Insurance is covered according to the standard public insurance at the University Hospital 
of North Norway (Norwegian: Norsk pasientskadeerstatning).  
 
12. Informed consent process 
The principal investigators will ensure that the participants are given full and adequate 
oral and written information about the clinical investigation, its purpose, risks and 
benefits, as well as inclusion and exclusion criteria. Subjects will be informed that they 
are free to discontinue their participation at any time without having to provide a reason, 
and that this will have no consequence for their treatment (patients) or employment 
(surgeons).  

Clinical Investigation Plan 
CIP-code:  
CIP-01.0 
34 
 
 
Subjects will be given the opportunity to ask questions and allowed time to consider the 
provided information and participation. If the person chooses to participate, both the 
subject and the investigator shall sign the informed consent form. A copy of the 
information and a copy of the informed consent form will be provided to the subject. The 
subject’s signed and dated informed consent must be obtained before performing any 
activity specific to the clinical investigation. The process will be documented in the 
subject’s source documents and the signed informed consents will be maintained with the 
essential documents. If new information becomes available that can significantly affect a 
subject’s future health and medical care, that information shall be provided to the affected 
subject(s) in written form. If new information is added to the clinical investigation, the 
subject has the right to reconsider whether he/she will continue their participation. 
 
Participation or non-participation in the NORspine follows established routines 
independently of this study.  
 
Informed consent process for vulnerable populations 
Patients are generally considered vulnerable. According to the established inclusion- and 
exclusion criteria for NORspine, we will not include children <18 years or patients unable 
to consent because of serious drug abuse, severe psychiatric disorders or language barriers  
[20].  A special informed consent process for particularly vulnerable populations is thus 
not needed.  
 
13. Adverse events, adverse device effects and device deficiencies 
Definitions 
 
Adverse event 
An adverse event (AE) is any untoward medical occurrence, disease or injury or clinical 
signs, in subjects, users or other persons, in the context of a clinical investigation, 
whether or not related to the investigational device. 
 
This definition includes events that are anticipated as well as unanticipated. This 
definition includes events occurring in the context of a clinical investigation related to the 
investigational device, the comparator or the procedures involved. 
 
Adverse device effect 
An adverse device effect (ADE) is any AE related to the use of an investigational medical 
device. This definition includes adverse events resulting from insufficient or inadequate 
instructions for use, installation, or any malfunction of the device. This definition 
includes any event resulting from use error or from intentional misuse. 
 

Clinical Investigation Plan 
CIP-code:  
CIP-01.0 
35 
 
In these studies, ADEs are any AE related to the DST and use or misuse of it, causing an 
erroneous outcome prediction which misinforms the SDM, and causes a risk for 
unjustified recommendations about the intervention (undergoing or not undergoing 
surgery). If not corrected by the surgeon’s discretion, this can lead to unwarranted advice 
recommending an operation for patients who will not benefit, or advice against an 
operation for patients who would have benefited from such treatment.  
 
Identifying such AEs is difficult, because for individuals, we will not know the outcome 
of the alternative treatment strategy, since undergoing one type of treatment (surgical or 
conservative) precludes observation of the outcome of the other. The crossover-rate from 
conservative to surgical treatment can be indicative, and will be registered, but assessing 
it will be difficult, since spondylosis is dynamic, implying that the indication for surgery 
change over time for the same individual. 
 
Recommendations considered to be obviously in conflict with present evidence and 
established best practice will be identified qualitatively through the participatory 
observation and interviews with the surgeons, counted and reported as possible AEs. 
 
Serious adverse event 
A serious AE (SAE) is any AE that led to any of the following: 
a) Death 
b) Serious deterioration in the health of the subject, that resulted in any of the 
following: 
i. 
Life-threatening illness or injury 
ii. 
Permanent impairment of a body structure or a body function 
iii. 
Hospitalization or prolongation of patient hospitalization 
iv. 
Medical or surgical intervention to prevent life-threatening illness or injury 
or permanent impairment to a body structure or a body function 
v. 
Chronic disease 
c) Fetal distress, fetal death or a congenital physical or mental impairment or birth 
defect 
Serious adverse device effect 
A serious adverse device effect (SADE) is an ADE that has resulted in any of the 
consequences characteristic of a SAE. Such events related to procedures imposed by the 
clinical investigation plan but not with the use of the device shall not be considered a 
SADE. Accordingly, SAEs occurring as complications to the operations are not 
considered SADEs, and thus not reported as such. 
 
Unanticipated Serious Adverse Device Effect 
An unanticipated SADE is an effect which by its nature, incidence, severity, or outcome 
has not been identified in the current risk assessment. SAEs related to procedures 

Clinical Investigation Plan 
CIP-code:  
CIP-01.0 
36 
 
imposed by the clinical investigation plan but not with the use of the DST shall not be 
considered SADEs. 
 
Device Deficiency 
A device deficiency (DD) is any inadequacy in the identity, quality, durability, reliability, 
safety or performance of an investigational device, including malfunction, use errors or 
inadequacy in information supplied by the manufacturer. 
 
In the present study, any obviously erroneous prediction produced by the DST, or 
erroneous predictions produced by user-error from patients or surgeons, should be 
recorded and reported accordingly.  
 
Recording and Reporting 
 
Recording 
All investigators will record all AEs, SAEs, SADEs and DDs according to the definitions 
above in the hospitals’ electronic reporting system for AEs (Docmap) within one work 
day after their occurrence. A simplified event analysis according to the hospitals’ regular 
routines will be done within two weeks. The two study sites use similar systems for 
recording and analyses of AEs. The coordinating investigator is responsible for the 
recording and the analyses. 
 
Reporting 
The principal investigators will report all SAEs, SADEs and DDs to the sponsor (UNN’s 
research director) immediately, but not later than three calendar days after the 
investigation site’s study personnel’s awareness of the event.  
 
The sponsor will report to the Norwegian medical products agency:  
• Any SAE and SADEs that has a causal relationship with the DST or the 
investigation procedure, or where such causal relationship is reasonably possible 
• Any DD that might have led to a SAE if appropriate action had not been taken, 
intervention had not occurred, or circumstances had been less fortunate 
• Any new findings in relation to any event referred to above 
Reporting by the sponsor will be done by the Summary reporting form (MDCG 2020-
10/2). The form will be updated for each reportable event and for new findings/updates to 
already reported events. For events that indicate an imminent risk of death, serious injury, 
or serious illness and that requires prompt remedial action for other patients/subjects, 
users or other persons or a new finding to it will be reported immediately, but not later 
than two calendar days after awareness by the sponsor of a new reportable event or of 
new information in relation with an already reported event. Any other reportable events 
or a new finding/update to it will be reported immediately, but not later than seven 

Clinical Investigation Plan 
CIP-code:  
CIP-01.0 
37 
 
calendar days following the date of awareness by the sponsor of the new reportable event 
or of new information in relation with an already reported event. 
 
Assessment of causality 
The relationship between each AE and the investigational device, the comparator and the 
investigation procedure will be assessed and recorded by the coordinating investigator 
and sponsor. The sponsor and investigator will distinguish between SAEs related to the 
DST and those related to the procedures (relatedness to both is possible).  
 
Each SAE will be classified according to four different levels of causality: 
1. Not related  
Relationship to the DST, comparator or procedures can be excluded when: 
i. 
The event has no temporal relationship with use of the DST, or the 
procedures related to use of it 
ii. 
The SAE does not follow a known response to use of the DST (if the 
response pattern is previously known) and is biologically implausible 
iii. 
The SAE can be attributed to another cause (e.g. an underlying or 
concurrent illness/ clinical condition, an effect of another device, drug, 
treatment or other risk factors) 
iv. 
The event does not depend on a prediction given by the DST 
In order to establish the non-relatedness, not all the criteria listed above must be met at 
the same time. 
 
2. Possible 
The relationship with the use of the DST or the related procedures is weak, but 
cannot be ruled out completely. Alternative causes are also possible (e.g. an 
underlying or concurrent illness, clinical condition or/and an effect of another 
device, drug or treatment). Cases where relatedness cannot be assessed, or no 
information has been obtained shall also be classified as possible. 
 
3. Probable 
The relationship with the use of the DST or the related procedures or the 
comparator, seems relevant and/or the event cannot be reasonably explained by 
another cause. 
 
 
 
 

Clinical Investigation Plan 
CIP-code:  
CIP-01.0 
38 
 
4. Causal relationship 
The SAE is associated with use of the DST or the related procedure, or the 
comparator or with procedures beyond reasonable doubt when: 
i. 
The event is a known side effect of the DST or the related procedures 
ii. 
The event has a temporal relationship with its use 
iii. 
The SAE follows a known response pattern to use of the DST (if the 
pattern is previously known) 
iv. 
Other possible causes (e.g. an underlying or concurrent illness, clinical 
condition or/and an effect of another device, drug or treatment) have been 
adequately ruled out 
v. 
Harm to the subject is due to error in use or interpretation of the prediction 
produced by the DST 
vi. 
The event depends on an erroneous prediction given by the DST 
In order to establish the relatedness, not all the criteria listed above might be met at the 
same time. 
 
List of foreseeable Adverse events 
In these studies, device-specific adverse effects are defined as erroneous outcome 
predictions which misinforms the SDM, and causes unjustified recommendations 
about the intervention (undergoing or not undergoing surgery). 
 
Possible foreseeable AEs are: 
1. User-error from a patient or a surgeon causes erroneous or incomplete recording 
of input data to the prediction algorithm, and this leads an erroneous prediction of 
the outcome 
2. Errors in data transfer between the different components of the SDM causes 
erroneous or incomplete input data to the prediction algorithm, and this leads to 
an erroneous prediction of the outcome 
3. The surgeon and/or the patient misinterpret the output from the prediction, and 
base the SDM on this misinterpretation 
4. The DST is applied to patients not fulfilling the inclusion criteria or to patients 
who should have been excluded. This would cause an irrelevant outcome 
prediction, and misinform the SDM. 
 
14. Premature termination of the clinical investigation  
The coordinating investigator, the sponsor, the Norwegian medical products agency, and 
the REC can all suspend or prematurely terminate or halt the investigation, if deemed 
necessary. The monitor can advise these actors to terminate the study. Premature 
termination of the investigation must be for significant and documented reasons.  

Clinical Investigation Plan 
CIP-code:  
CIP-01.0 
39 
 
 
If suspicion of an unacceptable risk to subjects arises, or when so instructed by the 
Norwegian medical products agency, the sponsor will suspend the investigation while the 
risk is assessed. The sponsor will terminate the investigation if an unacceptable risk is 
confirmed, and inform all investigators.  
 
The sponsor shall consider terminating or suspending the participation of a particular 
investigation site or investigator if the monitoring identifies serious or repeated deviations 
on the part of a site or an investigator. If the suspension or premature termination was in 
the interest of safety, the sponsor shall inform all other principal investigators. 
 
If, in the opinion of the principal investigator, clinical observations suggest that it may be 
unsafe to continue the investigation at the site, the investigator may terminate the site’s 
participation after consultation with the sponsor. A written statement documenting the 
reasons for such termination shall be provided to the sponsor.  
 
If the investigation is prematurely terminated, the investigators shall promptly inform the 
subjects and take necessary steps to finalize their engagement in the investigation. All 
relevant investigation material must be collected, and accountability completed. 
 
If the clinical investigation is interrupted or terminated prematurely, the sponsor will 
report to the Norwegian medical products agency within 15 days together with a 
justification. If the sponsor has temporarily halted or prematurely terminated the 
investigation on safety grounds, the Norwegian medical products agency will be 
informed within 24 hours. A clinical investigation report will be prepared within three 
months of the early termination or temporary halt, irrespective of the results. In the event 
that the investigation is restarted within three months after the temporary halt, the sponsor 
does not have to submit a clinical investigation report until the clinical investigation has 
been completed. The final clinical investigation report shall include details with respect to 
the temporary halt. If relevant, affected patients will be followed up after termination of 
the investigation by either telephone or outpatient clinic, depending on what the 
coordinating and principal investigators find appropriate.  
 
We consider the risk of events necessitating a premature or temporary termination as low 
due to the low-risk nature of the DST. However, we cannot preclude that the DST will be 
evaluated significantly less useable by surgeons who have not been participated in the 
development than by the involved surgeons. In such a case, there is a risk that the 
iterative redesign process can identify technical obstacles that are difficult to overcome. 
This can lead to temporary halting of the feasibility study (study 1), and in a worst-case 
scenario to termination of the study. 
 
 

Clinical Investigation Plan 
CIP-code:  
CIP-01.0 
40 
 
15. Publication policy 
This clinical investigation will be registered on ClinicalTrials.gov before the start of 
recruitment activities and the content will be updated throughout the conduct of the 
clinical investigation and the results entered at completion of the clinical investigation. 
 
A clinical investigation report according to MDR article 77 will be filed to the Norwegian 
medical products agency by the sponsor within one year after closure of the pilot study 
(study 2) and the results will thus be publicly available.  
 
The two studies will be submitted as separate publications to international peer-reviewed 
open-access journals within one year after completion of the recruitment (the feasibility 
study) and one year after completion of 12-month follow-up (the pilot study), 
independently of the findings. This means that negative outcomes will be published to 
mitigate publication bias. The sponsor will not take an active role in the publishing. The 
coordinating investigator is responsible for publishing the studies, and the  
criteria for authorship will follow the Vancouver Recommendations [39]. 
 
16. List of technical and functional features of the device 
The DST is an integrate compound of software-solutions in the DIPS Arena® EHR, the 
national clinical quality register NORspine, and an AI-enabled prediction algorithm 
specifically developed for the DST.  
 
Technical architecture 
The DST depends on four software components (Figure 2): 
1. DIPS Arena® which stores the questionnaires in a structured format and presents 
the user interface for the tool. 
2. The user interface, which provides the users access to the questionnaires and the 
predictions. 
3. The backend service, which retrieves predictive variables from the questionnaires, 
transfers them to the cloud-based model hosting service for the AI-enabled 
algorithm, and stores the predicted outcome in the patient record. 
4. The model hosting service, which performs the prediction. 
 
 

Clinical Investigation Plan 
CIP-code:  
CIP-01.0 
41 
 
           
 
 
Figure 2. The system architecture. Major components and the flow of data between them. 
 
Data flow 
The software requires that both the patient and the surgeon complete the baseline 
questionnaires in DIPS Arena® before a prediction of the outcome can be requested: 
 
1. The user interface is shown as an embedded application in DIPS Arena®, and the 
surgeon selects the questionnaires which he/she wants the prediction to be based 
on (by default, the last registered). 
2. The backend service retrieves the questionnaires and extracts the variables 
required by the algorithm. 
3. The backend transfers these variables (de-identified) to the model hosting service. 
4. The model hosting service operates the algorithm, computes an outcome prediction 
and provides explanations for the prediction. 
5. The backend service formats these results, transfers them to DIPS Arena® and 
presents them in the user interface, along with a summary of the information 
registered in the questionnaires. 
6. Finally, the prediction is stored as a separate document in the patient’s DIPS 
Arena® EHR. 
 
Details of development 
• The user interface is a web application developed in the Svelte framework, which 
implements the Substitutable medical applications and reusable technologies 
(SMART) on Fast healthcare interoperability resources (FHIR) standard for 
authentication and resource retrieval. 
• The backend service is a Python application that manages the data flow described 
above. It hosts the user interface and, once the user is authenticated, provides it 
access to predictions for specified questionnaires. It retrieves questionnaires from 
DIPS Arena®, selects the appropriate variables from the questionnaires, and 
transfers them to the prediction algorithm. 

Clinical Investigation Plan 
CIP-code:  
CIP-01.0 
42 
 
• The model host is a Python application, which provides a representational state 
transfer (REST) interface to computing predictions from an extreme gradient 
boosting (XGBoost) model.  
• DIPS Arena® manages authentication and authorization for the DST. Before use, a 
system administrator must authorize the user to access the application. When the 
user starts the DST, the tool requests an authorization token for the session from 
DIPS Arena®. Without this token, the user is not allowed to request patient 
questionnaires and cannot use the tool. 
• The backend service and user interface are deployed in the same network 
infrastructure as DIPS Arena®, while the cloud-based model hosting service is 
accessible through the National health network.  
• Data transfer between the components use an encrypted hypertext transfer 
protocol secure (HTTPS), through REST interfaces defined for each component 
with explicit API specifications. 
 
17.  Attachments 
 
1. Table displaying the workflow and roles in current routine practice and in the clinical 
investigation 
 
 

Clinical Investigation Plan 
CIP-code:  
CIP-01.0 
43 
 
18. Bibliography 
 
1. 
Tollånes, M.C., et al., Disease burden in Norway in 2016. Tidsskr Nor Laegeforen, 
2018. 138(15). 
2. 
Brage, S., et al., [Musculoskeletal disorders as causes of sick leave and disability 
benefits]. Tidsskr Nor Laegeforen, 2010. 130(23): p. 2369-70. 
3. 
Gibson, J.N., I.C. Grant, and G. Waddell, The Cochrane review of surgery for lumbar 
disc prolapse and degenerative lumbar spondylosis. Spine (Phila Pa 1976), 1999. 
24(17): p. 1820-32. 
4. 
Solberg, T.K., et al., Årsrapport 2022: Nasjonalt kvalitetsregister for ryggkirurgi, 
resultater og forbedringstiltak. Septentrio Reports, 2023(1). 
5. 
Wennberg, J.E., Unwarranted variations in healthcare delivery: implications for 
academic medical centres. Bmj, 2002. 325(7370): p. 961-4. 
6. 
Weinstein, J.N., et al., United States' trends and regional variations in lumbar spine 
surgery: 1992-2003. Spine (Phila Pa 1976), 2006. 31(23): p. 2707-14. 
7. 
Ingebrigtsen, T., et al., Treatment rates for lumbar spine surgery in Norway and 
Northern Norway Regional Health Authority 2014-18. Tidsskr Nor Laegeforen, 2020. 
140(17). 
8. 
Yap, Z.L., et al., The role of the social determinants of health in outcomes of surgery 
for low back pain: a systematic review and narrative synthesis. Spine J, 2022. 22(5): p. 
793-809. 
9. 
McGirt, M.J., et al., An analysis from the Quality Outcomes Database, Part 1. 
Disability, quality of life, and pain outcomes following lumbar spine surgery: 
predicting likely individual patient outcomes for shared decision-making. J Neurosurg 
Spine, 2017. 27(4): p. 357-369. 
10. 
Stacey, D., et al., Decision aids for people facing health treatment or screening 
decisions. Cochrane Database Syst Rev, 2024. 1(1): p. Cd001431. 
11. 
Khor, S., et al., Development and Validation of a Prediction Model for Pain and 
Functional Outcomes After Lumbar Spine Surgery. JAMA Surg, 2018. 153(7): p. 634-
642. 
12. 
Carreon, L.Y., et al., Performance of the streamlined quality outcomes database web-
based calculator: internal and external validation. Spine J, 2023. 
13. 
Lubelski, D., et al., Patient-specific prediction model for clinical and quality-of-life 
outcomes after lumbar spine surgery. J Neurosurg Spine, 2021. 34(4): p. 580-588. 
14. 
Fritzell, P., J. Mesterton, and O. Hagg, Prediction of outcome after spinal surgery-using 
The Dialogue Support based on the Swedish national quality register. Eur Spine J, 
2022. 31(4): p. 889-900. 
15. 
Pedersen, C.F., et al., PROPOSE. Development and validation of a prediction model for 
shared decision making for patients with lumbar spinal stenosis. N Am Spine Soc J, 
2024. 17: p. 100309. 
16. 
Pedersen, C.F., et al., Applied Machine Learning for Spine Surgeons: Predicting 
Outcome for Patients Undergoing Treatment for Lumbar Disc Herniation Using PRO 
Data. Global Spine J, 2022. 12(5): p. 866-876. 
17. 
De Silva, T., et al., SpineCloud: image analytics for predictive modeling of spine 
surgery outcomes. J Med Imaging (Bellingham), 2020. 7(3): p. 031502. 

Clinical Investigation Plan 
CIP-code:  
CIP-01.0 
44 
 
18. 
Müller, D., et al., Development of a machine-learning based model for predicting 
multidimensional outcome after surgery for degenerative disorders of the spine. Eur 
Spine J, 2022. 31(8): p. 2125-2136. 
19. 
Toh, Z.A., et al., Clinical Decision Support System Used in Spinal Disorders: Scoping 
Review. J Med Internet Res, 2024. 26: p. e53951. 
20. 
Mikkelsen, E., et al., The Norwegian registry for spine surgery (NORspine): cohort 
profile. Eur Spine J, 2023. 32(11): p. 3713-3730. 
21. 
Berg, B., et al., Machine Learning Models for Predicting Disability and Pain Following 
Lumbar Disc Herniation Surgery. JAMA Netw Open, 2024. 7(2): p. e2355024. 
22. 
Čartolovni, A., A. Tomičić, and E. Lazić Mosler, Ethical, legal, and social considerations 
of AI-based medical decision-support tools: A scoping review. Int J Med Inform, 2022. 
161: p. 104738. 
23. 
Eiskjær, S., et al., Usability and performance expectancy govern spine surgeons' use of 
a clinical decision support system for shared decision-making on the choice of 
treatment of common lumbar degenerative disorders. Front Digit Health, 2023. 5: p. 
1225540. 
24. 
Brøgger, H.M.B., et al., [Not Available]. Tidsskr Nor Laegeforen, 2023. 143(14). 
25. 
Berkovsky, S. and E. Coiera, Moving beyond algorithmic accuracy to improving user 
interaction with clinical AI. PLOS Digit Health, 2023. 2(3): p. e0000222. 
26. 
Bowen, D.J., et al., How we design feasibility studies. Am J Prev Med, 2009. 36(5): p. 
452-7. 
27. 
Arain, M., et al., What is a pilot or feasibility study? A review of current practice and 
editorial policy. BMC Med Res Methodol, 2010. 10: p. 67. 
28. 
Hara, S., et al., Return to Work After Surgery for Cervical Radiculopathy: A Nationwide 
Registry-based Observational Study. Spine, 2023. 48(4). 
29. 
Jacobs, W.C.H., et al., Surgery versus conservative management of sciatica due to a 
lumbar herniated disc: a systematic review. European Spine Journal, 2011. 20(4): p. 
513-522. 
30. 
Wessberg, P. and K. Frennered, Central lumbar spinal stenosis: natural history of non-
surgical patients. Eur Spine J, 2017. 26(10): p. 2536-2542. 
31. 
Fetters, M.D., L.A. Curry, and J.W. Creswell, Achieving integration in mixed methods 
designs-principles and practices. Health Serv Res, 2013. 48(6 Pt 2): p. 2134-56. 
32. 
Malterud, K., [Qualitative methods in medical research--preconditions, potentials and 
limitations]. Tidsskr Nor Laegeforen, 2002. 122(25): p. 2468-72. 
33. 
Palinkas, L.A., et al., Purposeful Sampling for Qualitative Data Collection and Analysis 
in Mixed Method Implementation Research. Adm Policy Ment Health, 2015. 42(5): p. 
533-44. 
34. 
Malterud, K., V.D. Siersma, and A.D. Guassora, Sample Size in Qualitative Interview 
Studies: Guided by Information Power. Qual Health Res, 2016. 26(13): p. 1753-1760. 
35. 
Guest, G., A. Bunce, and L. Johnson, How Many Interviews Are Enough?:An 
Experiment with Data Saturation and Variability. Field Methods, 2006. 18(1): p. 59-
82. 
36. 
Hamilton, A.B. and E.P. Finley, Qualitative methods in implementation research: An 
introduction. Psychiatry Res, 2019. 280: p. 112516. 
37. 
Johnson, J.L., D. Adkins, and S. Chauvin, A Review of the Quality Indicators of Rigor in 
Qualitative Research. Am J Pharm Educ, 2020. 84(1): p. 7120. 

Clinical Investigation Plan 
CIP-code:  
CIP-01.0 
45 
 
38. 
World Medical Association Declaration of Helsinki: ethical principles for medical 
research involving human subjects. Jama, 2013. 310(20): p. 2191-4. 
39. 
ICMJE, Recommendations for the Conduct, Reporting, Editing, and Publication of 
Scholarly Work in Medical Journals. 2023: https://www.icmje.org.