ORIGINAL SCIENTIFIC REPORT

Using a Hybrid Electronic Medical Record Systemfor the Surveillance of Adverse Surgical Events and Human Errorin A Developing World Surgical Service

Grant Laing • John Bruce • David Skinner •

Nikki Allorto • Colleen Aldous • Sandie Thomson •

Damian Clarke

� Societe Internationale de Chirurgie 2014

Abstract

Introduction The quantification and analysis of adverse events is essential to benchmark surgical outcomes and

establish a foundation for quality improvement interventions. We developed a hybrid electronic medical record

(HEMR) system for the accurate collection and integration of data into a structured morbidity and mortality (M&M)

meeting.

Methodology The HEMR system was implemented on January 1, 2013. It included a mechanism to capture and

classify adverse events using the ICD-10 coding system. This was achieved by both prospective reporting by clients

and by retrospective sentinel-event-trawling performed by administrators.

Results From January 1, 2013 to March 20, 2014, 6,217 patients were admitted within the tertiary surgical service

of Greys Hospital. A total of 1,314 (21.1 %) adverse events and 315 (5.1 %) deaths were recorded. The adverse

events were divided into 875 ‘‘pathology-related’’ morbidities and 439 ‘‘error-related’’ morbidities. Pathology-related

morbidities included 725 systemic complications and 150 operative complications. Error-related morbidities included

257 cognitive errors, 158 (2.5 %) iatrogenic injuries, and 24 (1.3 %) missed injuries. Error accounted for 439 (33 %)

of the total number of adverse events. A total of 938 (71.4 %) adverse events were captured prospectively, whereas

the remaining 376 (28.6 %) were captured retrospectively. The ICD-10 coding system was found to have some

limitations in its classification of adverse events.

Conclusions The HEMR system has provided the necessary platform within our service to benchmark the incidence

of adverse events. The use of the international ICD-10 coding system has identified some limitations in its ability to

classify and categorise adverse events in surgery.

G. Laing (&)

Surgery, UKZN, Durban, South Africa

e-mail: grantlaing@me.com

J. Bruce � N. Allorto � D. Clarke

General Surgery, Greys Hospital, Pietermaritzburg, South Africa

D. Skinner

Critical Care and Anaesthesia, King Edward VIII Hospital,

Durban, South Africa

C. Aldous

School of Clinical Medicine, King Edward VIII Hospital,

Durban, South Africa

S. Thomson

Gastrointestinal Unit University of Cape Town, Groote Schuur

Hospital, Cape Town, South Africa

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World J Surg

DOI 10.1007/s00268-014-2766-x

Introduction

The realisation that human error contributes significantly to

surgical adverse events has fostered interest in quality

improvement (QI) initiatives, which directly target human

error [1–3]. The traditional forum for the identification and

discussion of surgical adverse events has been the Mor-

bidity and Mortality (M&M) meeting. The M&M meeting,

however, has not always been an effective driver of

improved patient safety. The two most apparent reasons for

this failure include the lack of structures to facilitate an

understanding of the contribution of error to adverse events

and the lack of robust systems to capture both the incidence

and type(s) of adverse events and errors. We have

attempted to address the first deficiency by introducing

taxonomy of error into a structured M&M to analyse the

contribution of error to adverse events [4]. However,

underreporting of errors and adverse events has remained a

challenge and this is not unique to our institution. Most

adverse-events-surveillance-systems rely upon self-report-

ing and hence fail to capture the true incidence of both

adverse events and errors [5]. We have previously pub-

lished on the design, construction, and implementation of a

hybrid electronic medical record (HEMR) system within a

developing world tertiary surgical service [6]. This cost-

effective information system captured data at multiple

points in time (admission, operative intervention, endo-

scopic intervention, adverse events, and discharge). The

HEMR system combined handwritten documents and

printed medical records, hence its description as a hybrid

system, and merged the functions of a registry and an

electronic medical record (EMR). It was intended that the

HEMR system not only capture data for research purposes

but act as a driver of QI projects by facilitating work flow,

record keeping, and assist clinical management by the

inclusion of an integrated clinical decision support system

(CDSS). Careful attention was paid to integrating the

HEMR system into the ergonomic structure of the service,

by using it to replace or simplify preexisting tasks and to

support current educational initiatives. This study reviews

the use of the HEMR system to capture and aggregate data

pertaining to adverse events for analysis and discussion

within a structured weekly M&M meeting. It also quanti-

fies and classifies adverse events and errors within our

service with the intention of using these data to drive

ongoing QI programs.

Methodology

Ethical approval to construct and implement the HEMR

system was obtained before any development (ethics

number BCA221/13 BREC UKZN). The following terms

are defined for the purpose of clarity. An information

system, which combines an electronic registry with an

EMR system and a traditional paper-based record system,

can be referred to as a HEMR system. Clinical decision

support systems (CDSS) attempt to use information tech-

nology to improve the quality of care by supporting deci-

sion-making and directing staff down appropriate

management algorithms. The efficacy of self-reporting of

adverse events is dependent on the compliance of indi-

vidual clients. A sentinel event is an event in the process of

patient care, which is typically closely associated with

adverse events and as such, needs to be reviewed. Adverse

events have been classified into cases of ‘‘pathology-rela-

ted’’ morbidity and ‘‘error-related’’ morbidity for the pur-

poses of this study. Sentinel-event-trawling refers to the

independent retrospective review of all patients who have

experienced an adverse event, with the purpose of identi-

fying unreported cases of morbidity and error.

The HEMR system was designed to capture data at

admission, operation, endoscopy, and at discharge, trans-

fer, or death. At discharge, an ICD-10 diagnostic code was

assigned to each patient. Data relating to adverse events

were captured using both free-text descriptions and an

ICD-10 (Version 2010) classification. The identity of the

data capturer was documented as either a client or the

administrator, as well as whether it was prospectively or

retrospectively captured. The categories of data were

designed as a relational database, with the ability to create

unique, one-to-many relationships. Patients entered into the

HEMR system were automatically assigned a reference

identification (ID) number and their demographic details

were entered into the primary table. A number of second-

ary tables were related to the primary table, including

general, paediatric, and trauma surgery tables for admis-

sions and discharges, an operative table, an endoscopy

table, and a table for adverse events. The data model was

expanded into a series of digital layouts, which constitute

the system interface, where data pertaining to adverse

events would be entered using a combination of free-text

and predesigned dropdown menus with ICD-10 codes.

Fifteen desktop computers were positioned within the

hospital and local network connectivity enabled multiple

client access to the registry. The HEMR file was uploaded

via a host computer onto a secure server. Separate user-

names and passwords were created for the administrators

and clients. Automated hourly backups of the HEMR

system file were integrated into the design for data pro-

tection. The HEMR system was implemented at Greys

Hospital, Pietermaritzburg, Kwa-Zulu Natal, South Africa

on January 1, 2013. The estimated costs required for the

computer hardware and software for this project were

*150,000 South African rands ($15,720). No further fees

were required for external technologic support or added

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human resources. A training program was initiated for

clients involved in the use of the HEMR system and

included an introductory lecture with personal training

within a computer laboratory. As a quality assurance

measure the data entered into the HEMR system were

analysed and validated by the administrators on a weekly

basis.

Adverse event reporting mechanism

The interface for the adverse event reporting is illustrated

in Appendices in Figs. 4 and 6. This component relies on

prospective self-reporting by clients. The department

adopted and implemented a local policy, including a pre-

printed document for the conduct of morning and afternoon

ward rounds. This document includes a tick-box for the

presence or absence of new adverse events. Adverse events

identified by members of the surgical team were then

documented using the tick-box and thereafter prospectively

entered by the client onto the HEMR system. The design of

the HEMR system included automated reports for the

purpose of weekly departmental M&M meetings (Appen-

dix in Fig. 6). Data and statistics within these reports

included the total number of surgical admissions and dis-

charges, operations performed, adverse events, and deaths.

Sentinel-event-trawling

To complement the prospective self-reporting arm of the

system, an exercise of independent retrospective sentinel-

event-trawling was instituted. Administrators of the HEMR

system performed a retrospective analysis of patient

records to identify the incidence of unreported surgical

adverse events. Sentinel markers based on The American

College of Surgeons National Surgical Quality Improve-

ment Program (ACS-NSQIP) definitions of what consti-

tutes a complication and what should be reported to the

Northwestern Online Surgical Quality Improvement

(NOSQI) were used to identify unreported morbidity and

error. These included all complications (within 30 days of

surgery or at any time if related to the surgery), morbidities

in nonoperative patients, deaths, readmissions, unantici-

pated reoperations, admissions to ICU, prolonged hospital

stay, and specific complications, such a hospital-acquired

pneumonias, drug-related adverse reactions, and decubitus

pressure ulcers.

Classification and taxonomy

For the purposes of this study, we divided adverse events

up into ‘‘pathology-related’’ morbidities (which could be

either systemic complications or surgical complications)

and ‘‘error-related’’ morbidities (Fig. 1). Error was further

divided into cognitive failure, iatrogenic injury and missed

injury. Systemic complications were subclassified accord-

ing to the organ-system affected, and included cardiac,

urogenital, respiratory, neurological, gastrointestinal, hae-

matological, and psychiatric adverse events. Adjunct fail-

ure was classified as a systemic complication as was

multiple organ failure (MOF). Surgical complications

included haemorrhage, anastomotic leak, and uncontrolled

sepsis. Surrogate quality markers for a surgical service

included decubitus ulcers and nosocomial pneumonias.

Structured morbidity and mortality meeting

All surgical admissions were reviewed at the weekly M&M

meeting. Based on this peer-reviewed mechanism an

adverse event would be classified as either ‘‘disease-rela-

ted’’ or ‘‘error-related.’’ At the conclusion of the peer-

review process, all adverse events were allocated a final

ICD-10 code by the administrator. The ICD-10 codes were

reviewed to assess the adequacy of the existing system and

categorised into three broad categories: no corresponding

ICD-10 code; specific ICD-10 code; and nonspecific ICD-

10 code.

Results

From January 1, 2013 to March 20, 2014, a total of 6,217

patients were admitted within the Greys Metropolitan

Department of Surgery and 1,314 adverse events (excluding

deaths) were captured onto the HEMR system yielding an

incidence of 21.1 %. A total of 315 deaths were recorded

equating to mortality rate of 5.1 % (Table 1). The adverse

events were divided into 875 pathology-related morbidities

Fig. 1 Taxonomy of adverse events

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and 439 error-related morbidities (Fig. 1). Error accounted

for 33 % (439) of the surgical adverse events. The cases of

error-related morbidities included 257 cognitive errors

(Table 2), 158 (2.5 %) iatrogenic injuries (Table 3), and 24

(1.3 %) missed injuries. Pneumothorax following central

line insertion accounted for 25 % (n = 27) of iatrogenic

injuries (Table 3; Fig. 2). The pathology-related morbidi-

ties included 725 systemic complications and 150 surgical

complications (Table 4). Sepsis accounted for 34 %

(n = 301) of the pathology-related morbidities. Nosocomial

pneumonia (n = 100) and surgical site infection (n = 96)

collectively accounted for 65 % of all septic complications

(Fig. 3). Considering the total series numeric, our results

quantified an incidence of septic complications of 4.5 %

(301/6217). Of the adverse events, 938 (71.4 %) were

captured prospectively through client entry, whereas the

remaining 376 (28.6 %) were captured retrospectively

through the process of sentinel-event-trawling. The ICD-10

(version 2010) coding system was found to have limitations

in the classification of adverse events; 56 % had a highly

specific corresponding ICD-10 code, 25 % have a loosely

associated ICD-10 code, and 19 % of adverse events had no

identifiable ICD-10 code. This was particularly noticeable

with regards to the classification of medical error (Table 5),

missed, and iatrogenic injuries.

Discussion

Since the turn of the millennium when the Institute of

Medicine (IOM) published the monograph, ‘‘To Err is

Table 1 Total number of surgical admissions, adverse events, and

mortality rate

Admissions 6217

General surgery 3374

Trauma surgery 1792

Paediatric surgery 1051

Adverse events 1314 21.1 %

Pathology-related morbidities 875

Error-related morbidities 439

Deaths 315 5.1 %

Table 2 Cognitive error-related complications (subdivision of error-

related morbidities)

Cognitive errors 257

Error of omission 126

Error of commission 73

Cancellation OT 23

Error of assessment 22

Delay treatment 6

Error system failure 3

Error of planning 2

Error of assessment 1

Failed SNOM 1

SNOM selective nonoperative management

Table 3 Iatrogenic injury (subdivision of error-related morbidities)

Iatrogenic 158

Cardiac arrest 1

Adverse drug reaction 4

Warfarin toxicity 4

Fall (from stretcher) 6

Anaphylaxis 8

Unplanned adjunct removal 27

Anatomical injuries 108

Fig. 2 Iatrogenic injuries (N = 108)

Table 4 Pathology-related morbidities

Systemic 725

Sepsis 301

CVSa 106

Urogenital 81

Respiratory 77

Neurological 41

GITb 40

Metabolic 27

Systemic 26

Adjunct failure 13

HPBc 6

Hematological 4

Psychiatric 3

Operative 150

Total 875

a Cardiovascular systemb Gastrointestinal tract systemc Hepato-pancreatico-biliary system

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Human,’’ there has been a great deal of interest in error in

health care and in improving patient safety [1, 7–15]. The

ACS-NSQIP is the prototype of a surgically driven pro-

gram to improve quality of care and the central feature of

this initiative is the need for accurate data on adverse

events. MediBank is a South African software solution,

which has predominantly been utilised within the private

healthcare sector with approximately 36 participating

hospitals across four provinces [16]. The objectives of

MediBank have focused on performance improvement, the

optimisation of hospital operations, injury and disease

prevention, and medical research. The design, develop-

ment, and implementation of the HEMR system aimed to

pursue a comparable vision to MediBank. The HEMR

system has proven to be a simple, cost-effective informa-

tion solution, which can be implemented and maintained

within the service delivery of a government healthcare

institution—without requirements for additional research

assistants and information technology (IT) staff. The

HEMR system has enabled us to quantify the incidence and

spectrum of adverse events within our service by incor-

porating both a prospective self-reporting mechanism and

retrospective sentinel-event-trawling. Our results compare

with similar studies, which reported adverse event inci-

dences of 30–42 % [1, 4, 7, 8, 14].

The exclusive reliance on self-reporting of adverse events

results in underreporting and a consequent underestimation

of its incidence. Previous studies have estimated that

approximately one-third of adverse events and deaths are

never discussed at M&M meetings. There are many reasons

for the reluctance of doctors to report adverse events. The

culture of some M&M meetings may make full disclosure

less palatable. Cognitive dissonance may cause staff to

suppress poor outcomes. This is most apparent when staff

use the terms ‘‘nature of disease’’ or the ‘‘disease process’’

when discussing and accounting for complications. There is

a lack of appreciation of the systematic failures associated

with adverse outcomes. Staff members generally demon-

strate a tendency to defensiveness and anxiety concerning

the presentation of information relating to adverse events

and errors. In an attempt to improve this situation, North

Western University developed and implemented the NOS-

QI—a web-based, self-reporting system, which allowed for

the anonymous reporting of adverse events [5]. The authors

used a sentinel-event-trawling mechanism to compare

morbidity reported on the NOSQI with actual morbidity and

mortality rates by reviewing all readmissions and all hos-

pital recorded deaths on the preexisting hospital adminis-

trators North Western Event Tracking System (NETS).

They estimated that less than one-third of adverse events

were self-reported. Our results identified an improved

compliance rate with an incidence of underreporting of

28.6 %. This statistic illustrates that despite the implemen-

tation of a well-structured, planned, and executed depart-

mental program, underreporting is inevitable and suggests

retrospective sentinel-event-trawling systems and prospec-

tive self-reporting systems must work in conjunction to

obtain a more accurate estimate of adverse events.

Fig. 3 Sepsis-related morbidities (N = 301)

Table 5 ICD-10 limitations in the classification of error(s)

Adverse event N ICD-10 CODE

Error-related 439 Y60–Y69 Misadventures to patients during

surgical and medical care

Iatrogenic 158 Y60–Y69 Misadventures to patients during

surgical and medical care

Error of

omission

126 Y66 Nonadministration of surgical and

medical care

Error of

commission

73 No ICD-10 code

Missed injury 24 No ICD-10 code

Cancellation

OT

23 Z53.8 Procedure not carried out for other

reasons

Error of

assessment

22 No ICD-10 code

Delay

treatment

6 No ICD-10 code

Error system

failure

3 No ICD-10 code

Error of

planning

2 No ICD-10 code

Error of

assessment

1 No ICD-10 code

Failed

SNOM

1 No ICD-10 code

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The HEMR system has enabled us to quantify accurately

the impact of error on surgical adverse events and it would

appear that that 30–40 % of surgical adverse events could

be attributed to error. We describe a missed injury rate of

1.3 % amongst trauma patients and an iatrogenic injury

rate of 2.5 %. This compares with our previously reported

incidence of missed injuries of 2.5 %, which was estimated

during a 6-month period [9]. The ICD-10 coding system

appears to have limitations with regards to its classification

of human error, missed injury, and iatrogenic injury. With

the current focus on limiting the incidence of error in

surgical care, it is hoped that future versions of the ICD-10

coding system develop taxonomy for error-related adverse

events.

The quality of a surgical service is difficult to quantify

and requires the development of appropriate metrics for

benchmarking and measurement. As the quality of a health

care system is a multifaceted concept, obtaining an accu-

rate view of the efficiency of a system requires multiple

indicators [12–14]. Some of these indicators may be

qualitative as well as quantitative and must assess both the

process and the outcome of care. All quality markers are

dependent on accurate data collection for reliability, and

the HEMR system has functioned to meet these require-

ments. With these proven benchmarks, we hope to proceed

to develop and implement directed QI interventions to

improve outcomes and thereafter to accurately quantify the

impact of these programs.

Conclusions

The HEMR system has enabled us to capture accurately

and to analyse surgical adverse events. This was achieved

by combining a prospective electronic self-reporting

mechanism with a retrospective independent targeted

review system using sentinel-event-trawling. We have

integrated the HEMR system into the clinical workflow and

structure of our service. Although the study and interven-

tion cannot be proven to alter or improve patient outcomes,

it is hoped that that this will foster the development of

targeted QI programs to reduce the incidence and impact of

the most common adverse events identified through the

study. Targeted QI interventions to reduce the incidence of

nosocomial pneumonia, surgical site infection, and iatro-

genic pneumothorax (following central line insertion) are

the next priority based on our results. The ICD-10 coding

system was furthermore found to have some limitations

with regard to its classification of adverse events and it is

hoped that future editions will develop a mechanism to

improve its classification of error. It is possible that the

success of this system could be transferred to other clinical

disciplines.

Appendix

See Appendix Figs. 4, 5, and 6.

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Fig. 4 HEMR system interface: ICD-10 search engine

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Fig. 5 HEMR system interface: ICD-10 drop-down boxes and free-text description

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