Paper Based vs Electronic Based Clinical Records The effects on hospital statistics Class 18

Paper Based vs Electronic Based Clinical Records

The effects on hospital statistics

Class 18

Introduction

Recording clinical information is important to the health industry

Access to previous information leads to better diagnosis and treatment in the future [Movig KL, Leufkens HG, Lenderink AW, Egberts AC. Validity of hospital discharge International Classification of Diseases (ICD) codes for identifying patients with hyponatremia. J Clin Epidemiol. 2003 Jun;56(6):530-5.]

Paper-based record / Electronic records


Lacks of information of previous studies [Movig KL, Leufkens HG, Lenderink AW, Egberts AC. Validity of hospital discharge International Classification of Diseases (ICD) codes for identifying patients with hyponatremia. J Clin Epidemiol. 2003 Jun;56(6):530-5.]

Electronic patient record (EPR) as the main method in a near future.[Gillies J, Holt A. Anxious about electronic health records? No need to be. N Z Med J. 2003 Sep 26;116(1182):U604.]

It is important to study and to conclude if this new information organization will result in any difference from the old one.


Advantages and disadvantages of EPR and paper-based records. Most of the studies conclude that the electronic system is better, although some of them disagree. [ Van der Meijden MJ, Tange HJ, Boiten J, Troost J, Hasman A. An experimental electronic patient record for stroke patients. Part 1: situation analysis. Int J Med Inform. 2000 Sep;58-59:111-25]


Main difficulties in the implementation of EPR.[Mohr DN, Carpenter PC, Claus PL, Hagen PT, Karsell PR, Van Scoy RE. Implementing an EMR: paper's last hurrah. Proc Annu Symp Comput Appl Med Care. 1995;:157-61.]

Negligent data introduction.

[Friedman BA. The potential role of physicians in the management of hospital information systems. Clin Lab Med. 1990 Mar;10(1):239-50]

Typing and visualization speed.[Rodriguez NJ, Murillo V, Borges JA, Ortiz J, Sands DZ. A usability study of physicians interaction with a paper-based patient record system and a graphical-based electronic patient record system. Proc AMIA Symp. 2002;:667-71.]


Coding errors (ICD). [Movig KL, Leufkens HG, Lenderink AW, Egberts AC. Validity of hospital discharge International Classification of Diseases (ICD) codes for identifying patients with hyponatremia. J Clin Epidemiol. 2003 Jun;56(6):530-5.]

Costs and benefits of EPR.[Ellingsen G, Monteiro E. Big is beautiful: electronic patient records in large Norwegian hospitals 1980s-2001. Methods Inf Med. 2003;42(4):366-70.]

Hospital’s economical survival.[Muller ML, Burkle T, Irps S, Roeder N, Prokosch HU. The diagnosis related groups enhanced electronic medical record. Int J Med Inform. 2003 Jul;70(2-3):221-8.]


Objective

The main objective is:

Quantify the differences among diagnosis and procedures recorded before and after the EPR implementation in the paper-based and the electronic-based methods.


Methods

Participants and Methods

Type of study: it is longitudinal and observational

We chose two departments to work about (obstetrics and pneumology) and two periods of time (a few months before and after the implementation of EPR)

We treated two databases on SPSS®, each from one of the departments

Each database was divided in two periods, before and after the implementation


Inclusion criteria

We made a selection of the main diagnosis on each department: On the obstetrics department we chose those with a

minimum number of 20 cases on the sum of both periods On the pneumology department we chose those with a

minimum number of 10 cases on the sum of both periods

We did two kinds of analyses: a qualitative one and a quantitative one.


Methods – 1st PeriodFirst Period

Each DRG code consists on a general code that groups together several diagnoses into one variable

Each database was divided in two periods: Before and After EPR

Qualitative Analysis The qualitative analysis consists on checking the differences

between the GDH before and after the introduction of EPR

By using a statistical test (Chi-square) on SPSS® we have searched for significant differences between GDH from the two groups previously selected

After analyzing the differences in a major perspective we decided to check each GDH individually to quantify the differences by using another Chi-square test.


Quantitative Analysis

(it focuses on the number of the diagnosis rather than the type of diagnosis)

We started working with all the diagnosis instead of using only GDH in order to compare the number of diagnosis before and after the implementation

The main test used in this case was a Mann-Whitney test


Methods – 2nd Period


Second Period

The database used in the first period was changed into one that allowed us to check the frequency of each diagnosis/procedure separately.

The diagnosis/procedures selected were the fifteen with more cases on the sum of the before and after periods.

Using SPSS® the information was analised in two differente ways: General Approach to the data including the 15 diagnosis/procedures,

using a chi-square test. Each diagnosis/procedure was checked for differences between the

two periods of time, using chi-square tests.

Both doctors and coders from both departments were enquired in search for justification for the statistically significant differences found

Linear Flow Chart


Begining

Gantt map elaboration

Flow-chart construction

Introduction´s writting

Methods writting

Selection of a group of patients

(within each department)

Try to check out if there are “before and

after” significant differences

Try to find out na explanation for the

diffrences found

Discussion of results

Final work review

Webpage construction

End of work

Linear Flow Chart


Begining




Methods writting





Try to find out na explanation for the diffrences found


Final work review


End of work

Linear Flow Chart


Begining




Methods writting





Try to find out na explanation for the

diffrences found


Final work review


End of work

Methods Flow Chart


Begining

Is there any more time?

No

End of Work

Is p value bigger than alpha (α)?

Yes No

Yes

Selection of two departments:Pneumology and obstectrice

Selection of a group of pacients “before” and “after” within each

department

Checking out frequencies of diagnosis within the two groups

selected

H0: µ1=µ2H0: the database is the same before and after the

introduction of computerized systems

Descriptive statistics

t = (X1 - X2) - (µ1- µ2) / Sp √((1/ n1) + (1/ n2))

There are significant differences (H0 is

rejected)

There are no sgnificant differences

(H0 is accepted)

Finding out an explanation

More than 20 cases per diagnosis?

Looking for “before and after”

differencesExclude the

diagnosis in case

Yes No

Methods Flow Chart


Begining


No

End of Work


Yes No

Yes



department


selected




t = (X1 - X2) - (µ1- µ2) / Sp √((1/ n1) + (1/ n2))


rejected)


(H0 is accepted)





diagnosis in case

Yes No

Methods Flow Chart


Begining


No

End of Work


Yes No

Yes



department


selected




t = (X1 - X2) - (µ1- µ2) / Sp √((1/ n1) + (1/ n2))


rejected)


(H0 is accepted)





diagnosis in case

Yes No

Gantt’s Graphic/Chart


1st Period


1st Period

Results


Departament Before After

Obst 887 975

Pneumo 292 293

Table 1 - Total number of individuals on both departments, before and after the EPR implementation.

Results - Obstetrics department

others384383381380379373372371370362

GDH Diagnosis' code

60,0%

50,0%

40,0%

30,0%

20,0%

10,0%

0,0%

Per

cen

t

depois

antes

Graph 1 – Comparison between the two groups

GDH code

Diagnosis description

362 ENDOSCOPIC TUBAL INTERRUPTION

370 CESAREAN SECTION WITH CC

371 CESAREAN SECTION WITHOUT CC

372 VAGINAL DELIVERY WITH COMPLICATING DIAGNOSES

373 VAGINAL DELIVERY WITHOUT COMPLICATING DIAGNOSES

379 THREATENED ABORTION

380 ABORTION W/O D&C

381 ABORTION WITH D&C, ASPIRATION CURETTAGE OR HYSTEROTOMY

383 OTHER ANTEPARTUM DIAGNOSES WITH MEDICAL COMPLICATIONS

384 OTHER ANTEPARTUM DIAGNOSES WITHOUT MEDICAL COMPLICATIONS

Table 2 – Description of the GDH code from the selected diagnosis.




others384383381380379373372371370362

GDH Diagnosis' code

60,0%

50,0%

40,0%

30,0%

20,0%

10,0%

0,0%

Per

cen

t

depois

antes


Table 4 – % of each diagnosis on both periods

GDH cod

e

Before (%) After (%)

362 2,7 0

370 3,5 3,2

371 26,2 19,3

372 3,4 4,0

373 46,3 53,4

379 2,0 1,4

380 3,0 3,4

381 2,9 3,0

383 1,6 2,5

384 4,7 5,2

others 3,6 4,6



others384383381380379373372371370362

GDH Diagnosis' code

60,0%

50,0%

40,0%

30,0%

20,0%

10,0%

0,0%

Per

cen

t

depois

antes


Chi-Square Tests

45,670a 10 ,000

54,925 10 ,000

1,203 1 ,273

1862

Pearson Chi-Square

Likelihood Ratio

Linear-by-LinearAssociation

N of Valid Cases

Value dfAsymp. Sig.

(2-sided)

0 cells (,0%) have expected count less than 5. Theminimum expected count is 11,43.

a.



Diagnosis: “Parto Vaginal sem complicações” (GDH373)

In a first phase we searched for general differences in the GDH.Secondly we searched for differences on each individual GDH. This is an example of a GDH where we found statistically significant differences. The other cases are: “Cesariana sem complicações” (GDH371); and “Laqueação de trompas via laparoscópia” (GDH362).



Descriptives

2,5175 ,03716

2,4445

2,5904

2,4376

2,0000

1,225

1,10686

1,00

6,00

5,00

1,00

1,067 ,082

1,123 ,164

2,5590 ,03790

2,4846

2,6334

2,4655

2,0000

1,401

1,18355

1,00

6,00

5,00

1,00

1,177 ,078

1,170 ,156

Mean

Lower Bound

Upper Bound

95% ConfidenceInterval for Mean

5% Trimmed Mean

Median

Variance

Std. Deviation

Minimum

Maximum

Range

Interquartile Range

Skewness

Kurtosis

Mean

Lower Bound

Upper Bound


5% Trimmed Mean

Median

Variance

Std. Deviation

Minimum

Maximum

Range

Interquartile Range

Skewness

Kurtosis

Antes_ou_Depoisantes

depois

n_ddxStatistic Std. Error



Although it seems obvious that this isn’t a normal distribution we decided to confirm by doing an one sample K-S Test.



One-Sample Kolmogorov-Smirnov Test

887

2,5175

1,10686

,288

,288

-,194

8,565

,000

975

2,5590

1,18355

,298

,298

-,196

9,307

,000

N

Mean

Std. Deviation

Normal Parameters a,b

Absolute

Positive

Negative

Most ExtremeDifferences

Kolmogorov-Smirnov Z

Asymp. Sig. (2-tailed)

N

Mean

Std. Deviation


Absolute

Positive

Negative





depois

n_ddx

Test distribution is Normal.a.

Calculated from data.b.

Ranks

887 929,36 824343,50

975 933,45 910109,50

1862


depois

Total

n_ddxN Mean Rank Sum of Ranks

Test Statisticsa

430515,5

824343,5

-,175

,861

Mann-Whitney U

Wilcoxon W

Z


n_ddx

Grouping Variable: Antes_ou_Depoisa.

Mann-Whitney

The 1-sample K-S test confirmed that it wasn’t a normal distribution as

p<0,01 , so it is <0,05.

As it wasn’t a normal distribution we had to do a non-parametric

test for two independent variables.As we can see p=0,861 which is >0,05, so we accept nule

hypothesis.

Results – Pneumology department


others475,00101,0099,0095,0094,0092,0089,0088,0085,0082,0079,0076,0075,00

SELECT

40,0%

30,0%

20,0%

10,0%

0,0%

Per

cen

t

depoisantes

GDH code Diagnosis description

75 MAJOR CHEST PROCEDURE

76 OTHER RESPIRATORY SYSTEM OR PROCEDURES WITH CC

79 RESPIRATORY INFECTIONS AND INFLAMATIONS, AGE >17, WITH CC

82 RESPIRATORY NEOPLASM

85 PLEURAL EFFUSION WITH CC

88 CHRONIC OBSTRUCTIVE PULMONARY DISEASES

89 PNEUMONIA AGE 18 OR OLDER WITH COMPLICATION, BACTERIAL, VIRAL, AND BRONCHIAL PNEUMONIA AND

PLEURISY.

92 INTERSTICIAL LUNG DISEASE WITH CC

94 PNEUMOTHORAX WITH CC

95 PNEUMOTHORAX WITHOUT CC

99 RESPIRATORY SIGND AND SYMPTOMS WITH CC

101 OTHER RESPIRATORY SYSTEM DIAGNOSES W CC

475 RESPIRATORY SYSTEM DIAGNOSIS WITH VENTILATION SUPPORT


Table 5 – Description of the GDH code from the selected diagnosis.



others475,00101,0099,0095,0094,0092,0089,0088,0085,0082,0079,0076,0075,00

SELECT

40,0%

30,0%

20,0%

10,0%

0,0%

Per

cen

t

depoisantes


GDH code Before (%) After (%)

75 1,7 2,7

76 2,7 2,1

79 8,5 6,8

82 37,2 26,7

85 2,0 2,1

88 10,2 11,6

89 5,8 7,5

92 2,0 2,1

94 1,0 3,1

95 4,8 5,1

99 2,4 2,7

101 2,4 5,8

475 1,4 2,4

Others 17,7 19,2

Table 7 – % of each diagnosis on both periods



others475,00101,0099,0095,0094,0092,0089,0088,0085,0082,0079,0076,0075,00

SELECT

40,0%

30,0%

20,0%

10,0%

0,0%

Per

cen

t

depoisantes


Chi-Square Tests

15,796a 13 ,260

16,110 13 ,243

,214 1 ,644

585

Pearson Chi-Square

Likelihood Ratio

Linear-by-LinearAssociation

N of Valid Cases

Value dfAsymp. Sig.

(2-sided)

0 cells (,0%) have expected count less than 5. Theminimum expected count is 5,49.

a.



Diagnosis:“Neoplasias Respiratórias”(GDH82) Regarding pneumology, we followed the same procedure we used for obstretics. We searched for differences both generally and individually for significant GDH’s. Here is one exemple we found. The other significant GDH we found was “Outros diagnósticos de aparelho respiratório com complicações” (GDH101).



Descriptives

8,1877 ,28006

7,6365

8,7389

7,8790

7,0000

22,982

4,79393

1,00

20,00

19,00

5,50

,936 ,142

,272 ,284

7,4144 ,25710

6,9084

7,9204

7,0738

7,0000

19,302

4,39340

1,00

20,00

19,00

6,00

1,050 ,143

1,100 ,284

Mean

Lower Bound

Upper Bound


5% Trimmed Mean

Median

Variance

Std. Deviation

Minimum

Maximum

Range

Interquartile Range

Skewness

Kurtosis

Mean

Lower Bound

Upper Bound


5% Trimmed Mean

Median

Variance

Std. Deviation

Minimum

Maximum

Range

Interquartile Range

Skewness

Kurtosis

Antes_e_Depoisantes

depois

n_diagStatistic Std. Error



Although it seems obvious that this isn’t a normal distribution we decided to confirm by doing an one sample K-S Test.



One-Sample Kolmogorov-Smirnov Test

293

8,1877

4,79393

,147

,147

-,095

2,517

,000

292

7,4144

4,39340

,111

,111

-,075

1,903

,001

N

Mean

Std. Deviation


Absolute

Positive

Negative




N

Mean

Std. Deviation


Absolute

Positive

Negative




Antes_e_Depoisantes

depois

n_diag

Test distribution is Normal.a.

Calculated from data.b.

Ranks

293 305,00 89366,00

292 280,96 82039,00

585

Antes_e_Depoisantes

depois

Total

n_diagN Mean Rank Sum of Ranks

Test Statisticsa

39261,000

82039,000

-1,726

,084

Mann-Whitney U

Wilcoxon W

Z


n_diag

Grouping Variable: Antes_e_Depoisa.

Mann-Whitney

The 1-sample K-S test confirmed that it wasn’t a normal distribution as

p<0,01 , so it is <0,05.

As it wasn’t a normal distribution we had to do a non-parametric

test for two independent variables.As we can see p=0,084 which is >0,05, so we accept nule

hypothesis.

2nd Period


2nd Period

others

664,01

661,21

661,11

661,01

659,71

658,11

656,11

654,21

650,00

648,81

648,21

644,21

631,00

V27.0

V25.2

Dia

gn

osi

s

40,0%30,0%20,0%10,0%0,0%

PercentAfterBefore



General (differences were found)

Pearson Chi-Square126,435(

a)15 ,000

Likelihood Ratio134,712 15 ,000

Linear-by-Linear Association 9,705 1 ,002

N of Valid Cases4728

Value dfAsymp. Sig.

(2-sided)

a 0 cells (,0%) have expected count less than 5. The minimum expected count is 24,56.

Diagnosis - Obstetrics



Diagnosis - Obstetrics

Diagnosis where differences were found

V25.2 - Request for sterillization654.21 - Previous cesarean section664.01 – First-degree perineal laceration661.11 - Secondary uterine inertia648.21 - Anemia656.11 - Rhesus isoimmunization

others

664,01

661,21

661,11

661,01

659,71

658,11

656,11

654,21

650,00

648,81

648,21

644,21

631,00

V27.0

V25.2

Dia

gn

osi

s

40,0%30,0%20,0%10,0%0,0%

PercentAfterBefore



Procedures - Obstetrics


Pearson Chi-Square532,269(

a)15 ,000




Value dfAsymp. Sig.

(2-sided)

0 cells (,0%) have expected count less than 5. The minimum expected count is 58,69.

others

99,29

99,21

99,18

93,57

90,59

88,78

75,35

75,34

74,10

73,60

73,59

72,71

57,94

3,91

3,90

Pro

ced

ure

s

15,0%10,0%5,0%0,0%

PercentAfterBefore




Procedures where differences were found

75.35 - Other diagnostic procedures on fetus and amnion99.18 - Injection or infusion of electrolytes73.59 - Other manually assisted delivery90.59 - Microscopic examination of blood88.78 - Diagnostic ultrasound of gravis uterus73.6 - Episiotomy75.34 -Fetal monitoring, not otherwise specified99.29 - Injection or infusion of other therapeutic or prophylactic substance72.71 -Vacuum extraction with episiotomy93.57 - Aplication of other wound dressing

others

99,29

99,21

99,18

93,57

90,59

88,78

75,35

75,34

74,10

73,60

73,59

72,71

57,94

3,91

3,90

Pro

ced

ure

s

15,0%10,0%5,0%0,0%

PercentAfterBefore

Results - Pneumology department



Diagnosis - Pneumology

Pearson Chi-Square26,081(a) 15 ,037




Value dfAsymp. Sig.

(2-sided)


others

786,30

519,80

512,80

511,90

491,21

486,00

428,00

401,90

305,10

285,90

250,00

198,30

162,90

162,30

V15.82

Dia

gn

osi

s

80,0%60,0%40,0%20,0%0,0%

PercentAfterBefore





Doenças do aparelho respiratório, NCOP - 519.8

Derrame pleural não especificado - 511.9

Pneumonia devida a microrganismo não especificado - 486

others

786,30

519,80

512,80

511,90

491,21

486,00

428,00

401,90

305,10

285,90

250,00

198,30

162,90

162,30

V15.82

Dia

gn

osi

s

80,0%60,0%40,0%20,0%0,0%

PercentAfterBefore



Procedures - Pneumology


Pearson Chi-Square23,089(a) 15 ,082




Value dfAsymp. Sig.

(2-sided)


others

99,29

99,23

99,21

99,19

99,18

93,96

93,94

90,59

89,65

89,52

87,49

87,44

87,41

34,91

34,04

Pro

ced

ure

s

30,0%20,0%10,0%0,0%

PercentAfterBefore





Injecção ou infusão de electrolitos - 99.18

others

99,29

99,23

99,21

99,19

99,18

93,96

93,94

90,59

89,65

89,52

87,49

87,44

87,41

34,91

34,04

Pro

ced

ure

s

30,0%20,0%10,0%0,0%

PercentAfterBefore


Admissão para esterilização - V25.2

História de parto por cesariana, parto referenciado, c/ ou s/ condição anteparto - 654.21

Laceração períneo do primeiro grau - 664.01

Inércia uterina secundária - 661.11

Anemia complicando um parto referenciado, com ou sem menção de condição anteparto - 648.21

Isoimunização Rh condição anteparto com parto referenciado - 656.11

Discussion - Obstetrics department


Diagnosis - Obstetrics The increase in the number of cases, may possibly be justified by the attempt to reduce the practise of episiotomy.

Discussion - Obstetrics department




Actos de diagnóstico no feto e âmnio NCOP - 75.35

Parto manual assistido, não classificável em outra parte - 73.59

Exame microscópico NCOP do sangue - 90.59

Ultra - 88.78

Episiotomia - 73.6

Monitorização fetal NCOP - 75.34 (*)

Extracção por ventosa com episiotomia - 72.71


Injecção ou infusão de substancia terapêutica - 99.29

Aplicação de outros pensos para feridas - 93.57

the increase of the registration of this procedures contradicts the increase of the diagnose 664.01 (Laceração do perínio do primeiro grau). We found no explanation for this fact.

it increased because it became indispensable in every case of non tranquilizer fetal state.

Large increase of cases. There are two possible reasons: more available devices; and the existence of a phd theses in course, related to the decrease of the cesarians by non tranquilizer fetal state.

The decrease of this three procedures are the result of an intentional reduction of the registration of this kind of methods, because they are a kind of procedure that is done in almost every treatment and it not necessary neither useful to keep this type of information.

Discussion - Pneumology department




Doenças do aparelho respiratório, NCOP - 519.8

Derrame pleural não especificado - 511.9

Pneumonia devida a microrganismo não especificado - 486

The increase of this diagnoses is related to the management of the distribution of the patients between departments. The patients were usually randomly distributed between the internal medicine and the pneumology departments. But recently, the pneumology department is taking care of the delicated cases, like the ones above, which justifies the difference found.

Discussion – Pneumology department





The decrease of this procedure is the result of an intentional reduction of the registration of this kind of methods, because they are a kind of procedure that is done in almost every treatment and it not necessary neither useful to keep this type of information.

Diagnosis/Procedures Code Increase/ Decrease %

Diagnosis Obstetrics

Request for sterillization V25.2 Decrease -64,29%

Previous cesarean section 654.21 Decrease -48,28%

First-degree perineal laceration 664.01 Increase84,6

2%

Secondary uterine inertia 661.11 Decrease -88,89%

Anemia 648.21 Decrease -50,00%

Rhesus isoimmunization 656.11 Increase 14,06%

Procedures Obstetrics

Other diagnostic procedures on fetus and amnion75.35 Increase

48,19%

Other manually assisted delivery 73.59 Increase 43,06%

Microscopic examination of blood 90.59 Increase

35,59%

Diagnostic ultrasound of gravis uterus 88.78 Increase 42,31%

Episiotomy 73.6 Increase25,4

2%

Fetal monitoring, not otherwise specified 75.34 Increase 21,43%

Vacuum extraction with episiotomy 72.71 Increase 37,93%

Injection or infusion of electrolytes 99.18 Decrease -73,68%

Injection or infusion of other therapeutic or prophylactic substance 99.29 Decrease -53,62%

Aplication of other wound dressing 93.57 Decrease

-96,43%

Diagnosis Pneumology

Other diseases of respiratory system, not elsewhere classified 519.8 Increase

47,37%

Unspecified pleural effusion 511.9 Increase 55,56%

Pneumonia, organism unspecified 486 Increase 88,89%

Procedures Pneumology

Injection or infusion of electrolytes 99.18 Decrease -34,29%

AknowledgmentsWe would like to thank the professors Ricardo Correia,

Cristina Santos and Clara Tavares, without whose helpwe would not be able to finish our work.

We are also grateful to Dr. Fernando Lopes,responsible EPR codification, Dra. Ana Rosa and Dra.Raquel Mota, from the obstetrics department, and finally Dr. Agostinho Marques, from the pneumology department.

Finally, we would like to thank to Professor Altamiro,for his precious help and guidance throughout the year.


Documents

Paper Based vs Electronic Based Clinical Records The effects on hospital statistics Class 18