| Sequence Alignment and Location Tracking |
|
||
|
|||
|
Application of
Sequence Alignment Algorithms in Location Tracking Data to Determine Patient State in a Clinical Process Mark Meyer, MD MPH Laboratory of Computer
Science Harvard-MIT Division of
Health Sciences and Technology HST951 Final Project Abstract: Location tracking systems are
becoming more widespread in healthcare, but novel applications using the wealth
of data produced are still sparse.
Utilizing a sequence alignment algorithm, we have demonstrated their application
in healthcare with location tracking data to determine the type of patient
based on predefined templates and the current stage of a patient in a clinical
process. Future work will further refine
the sequence alignment algorithm for use with location tracking data and
clinical systems. While retail industries and
defense establishment, represented by Wal-Mart and the Department of Defense,1,2
have embraced RFID and location tracking systems, their application in the
healthcare setting is still in its infancy.
However, such systems hold great promise to streamline patient care,
manage assets and provide a safer hospital environment.Massachusetts General Hospital and Brigham & Women’s Hospital have installed the
Radianse location tracking system, a system
consisting of a network of receivers and tags that utilize both active RFID and
IR to determine location. The use of
real-time location data has already been demonstrated at Massachusetts General
Hospital to aid in correcting “wrong patient-wrong location” errors in the
operating room3 and the concept of real-time patient routing systems
has been proven using barcode scanning to track patients through the perioperative process and in the emergency department.4,5 The use of an RFID-based location tracking
system provides a more robust, sophisticated and automated mechanism to track
patient progress through a clinical workflow and to isolate and identify areas
in the hospital workflow where efficiency and patient satisfaction may be improved. The data stream produced by a
location tracking system may be represented by a sequence of receivers
consisting of the path taken by an individual tag. As the tag and the item or individual to
which the tag is attached moves from point to point, a continuous data stream is
produced consisting of the receivers encountered between these two points. However, there are issues with location
tracking systems leading to a low signal-to-noise ratio and a challenge in the
correct interpretation of such data. The
Radianse system in particular has a ten second
temporal resolution thus if one is able to reach, traverse, and exit a
receiver’s effective radius, then that receiver will not appear in the tag’s
history of travel. In addition, the
system may “lose” and later “find” a tag that has not actually changed
location. One very frequent reason for
such a finding is that the patient rolled onto the tag or otherwise is
obstructing its function. Under these
circumstances, the IR component does not help maintain a connection with the
system and the result is that the tag will occasionally drop off the system or
the tag will jump between spatially related receivers. To address issues of
information integrity and quality, a mechanism is needed to address the large
amount of noise present in the data stream.
We hypothesize that utilizing sequence alignment algorithms, originally
used in molecular biology, will help compensate for the location data stream’s
inherent fuzziness and may be used to infer both type of patient based on the
sequence of locations they visit and the current state in an already mapped
clinical process. Methods Sequence Alignment: The sequence alignment
algorithm was written utilizing PHP scripting.
We utilize the alignment algorithm where we let:
Then an alignment of x and y is:
both hold. We additionally define the function d which represents
the “distance” between the elements of the union of Σ and d(a,-) = d(-,a) = g(a) for some suitable function g. Therefore, d(a,b) is the cost of mutating a to b and g(a) is the cost of
inserting or deleting the letter a. We begin by constructing a matrix representing all
possible moves to create all possible sequence alignments. We arbitrarily define the data stream to be x
and the template to be y. We also have
defined g=1, d(a, a)=0 for all a, and d(a, b) = 1 for
all distinct a and b. We define D0,0=0. For each
move, we select the minimum of the three possible moves into the square. If we are trying to allocate Di,j then we select the min To reconstruct the optimal sequence, we have that:
We use these rules, starting at Dn,m, to reconstruct the optimal sequence. At any point Di,j, we select the next move to be min Location Data: The location tracking data was cleaned utilizing a PHP
script. The location tracking data
originally is obtained as a mySQL database and
through a custom query, all events for a tag may be
extracted. This raw data is furthered
processed to eliminate duplicate entries and to eliminate uninformative
events. There are a total of
twenty-three events in the Radianse system. Events retained during the data cleaning
process include:
Workflow
Template: We have selected needle localization for breast biopsy
patients as the test subjects in this study.
This group of patients is ideally suited for study in this application
due to their clinical process. During
the course of a patient’s clinical workflow in needle localization, they spend
time both in surgery and radiology. As
such, they visit the same clinical location multiple times as they progress
through different stages in their care.
Therefore, location does not paint a complete picture as the same
location may represent very different clinical stages. We have created a template for needle localization
patients represented as: ‘chk’,
‘wt’, ‘chng’, ‘sdsur’, ‘hall’,
‘exam’, ‘hall’, ‘trha’, ‘tr’,
‘sdsur’ where:
There may be many variations in this template as
patients may visit, for instance, the same day surgery or transient recovery
holding areas several times in their clinical process. Results The initial cleaned location tracking data that we used
is as follows:
This then became the sequence: 'chk',
'wt', 'chng', 'chk', 'tt', 'trha', 'htrha',
'rnst', 'trha', 'sdsur', 'sdsur', 'sdsun', 'sdsur', 'sdsun', 'sdsur', 'rwt', 'rwt', 'exam', 'exam',
'exam', 'hall', 'trha', 'htrha',
'trha', 'tt', 'tr', 'htrha', 'sdsur', 'sdsun' We used this sequence as our data and the needle
localization template: 'chk',
'wt', 'chng', 'sdsur',
'hall', 'exam', 'hall', 'trha', 'tr',
'sdsur' We obtained the following matrix from the sequence
alignment algorithm:
Dn,m is 20. This
corresponds to the optimal alignment solution:
If we consider an alternate clinical workflow for
non-needle localization breast biopsy patients: 'chk',
'wt', 'chng', 'htrha', 'trha', 'htrha', 'tr', 'pacu' This is similar to a needle localization patient but
removes the time in radiology and has the patient leave the OR for the
post-anesthesia care unit (PACU). We
then obtained the matrix:
Dn,m is 22. We investigated using the original needle localization
template and a truncated data stream: 'chk',
'wt', 'chng', 'chk', 'tt', 'trha', 'htrha',
'rnst', 'trha', 'sdsur', 'sdsur' to assess the ability of the algorithm to determine the
current step in the clinical process.
This truncated data stream represents a patient who is yet to leave for
the radiology suite which is their next step.
Stepping iteratively through the template using g(a)=2
and d(a,b)=3, we obtained:
The minimum occurred with a template of ('chk', 'wt', 'chng', 'sdsur') and a cost of 14. Discussion Utilizing the cleaned location data stream, the
resulting matrix had an optimal score of 20 and successfully matched the noisy
data stream with the correct stage of the clinical process. In particular, the test patient prematurely
visited the transient recovery holding area and the location tracking system
did not locate the patient in the hallway to the radiology suite, a sentinel
event that the patient is on their way to or from radiology. Despite this, it correctly entered gaps into
the template that resulted in an accurate representation of the patient’s
current state; it matched the transient recovery holding area after the patient
was in radiology and made a substitution prior to the radiology exam room to
account for the missing hall location. When the template was changed to represent a different
clinical workflow, specifically breast biopsy patients not undergoing needle
localization, the resulting optimal score was 22, greater than the 20 found previously
when matching to a needle localization template. This would lead one to conclude that this
data comes from a needle localization patient or that it is from a non-needle
localization breast biopsy patient for whom there was a significant process exception
which has caused their workflow to more represent a needle localization
patient. When we break the needle localization template into
steps where a template string represents the current step and all previous
ones, we can run the sequence alignment algorithm against each of these and
presumably the one with the lowest score will be the template demonstrating the
current step of the clinical process.
After truncating the data stream to represent location data for a
patient still in the same day surgery unit whose next step is to go to
radiology, we found the minimum optimal score corresponding to a template at
the same stage as the data stream. We
were therefore successfully able to extract the stage in a clinical process
from purely the location data and knowledge of the clinical process in the form
of a template. There are two primary parameters, g(a)
and d(a,b), that can be changed to influence the
results of the sequence alignment algorithm.
For much of the initial work, both of these were set to 1. However, for determining the step in a
clinical workflow, it was necessary to increase d(a,b) relative to g(a) such that there would be more penalty
for making a substitution. Since
clinical steps that are not used in the template would result in a
substitution, this causes more of a penalty for these templates that are too
long and include steps beyond the current step of the data stream. In this instance, d(a,b)=3 and g(a)=2 worked well in slightly penalizing a
substitution that would incorrectly advance a sequence beyond its correct
current step. In future work, additional refinements will be made to
the sequence alignment algorithm along with additional work on the templates
and location data processing. In
particular, it would be beneficial to map physical receiver locations to
generic locations or stages in a clinical process to improve the generalizability of the process. For instance, instead of having ‘exam’ as a
location, specifically the exam room, locations such as this would come under
the auspices of the general category ‘radiology.’ This mapping to more general categories would
abstract the templates and more easily allow the process to be applied to other
institutions and other location tracking systems. This would also allow the alignment algorithm
to take into account repeated locations in a general area to improve its
results. For instance, if the individual
has moved between several of the radiology receivers, this is better evidence
that the patient is actually located there and at that clinical step instead of
being an anomalous reading or transient movement through an area. Additional systems may easily be integrated, allowing
for more evidence concerning a patient’s stage in a clinical process. Information from radiology systems or perioperative records can help augment decisions concerning
a patient’s location in a clinical process, helping to provide a complete
picture of patient status. However,
standing on its own, location data serves adequately. We have shown that sequence alignment
techniques can successfully be used solely with location tracking data to
determine the type of patient and the patient’s current stage in an established
clinical workflow. Conclusion We have successfully utilized sequence alignment algorithms
to serve a functional role in location tracking data. Specifically, we have demonstrated that
sequence alignment algorithms may be utilized to determine the type of patient
from which a data stream has come along with determining the location in a
clinical process a particular data stream currently is. We have demonstrated this through the
implementation of a sequence alignment algorithm using PHP scripting and
evaluating sets of test data. Future
work will further develop this concept with modifications to the algorithm to
address factors of location data sequences not present in nucleotide sequences. References 1. Navy field hospital tests patient tracking in 2. Want R. RFID: A Key to Automating Everything.
Scientific American Jan2004;290(1):56-65. 3. Sandberg WS, Hakkinen M,
Egan M, Curran PK, Fairbrother P, Choquette
K, Daily B, Sarkka JP, Rattner
D: Automatic detection and notification of "wrong patient-wrong location''
errors in the operating room. Surg Innov 2005; 12: 253-60 4. Rotondi A, Brindis C, Cantees K, DeRiso B, Ilkin H, Palmer J, Gunnerson H, Watkins WD. Benchmarking the Perioperative Process. I. Patient Routing Systems: A Method
for Continual Improvement of Patient Flow and Resource Utilization. Journal of
Clinical Anesthesia 1997;9:159-69. 5. Amber R, Appendix 1 The PHP script used to extract and clean location
tracking data from the Radianse database. <html> <head> <title>Needle Localization Patient
Information</title> </head> <body> <link
rel="stylesheet" href="style.css"
type="text/css"> <?php $host
= ""; $user
= ""; $password
= ""; mysql_connect($host,
$user, $password); mysql_select_db("radianse"); $query
= "select * from patienttable where patienttypecode = 3 order by patienttableindex desc"; $needle_pats = mysql_query($query); $num_needle_pats = mysql_num_rows($needle_pats); //for
loop selects out the needle loc patients for ($i=0; $i
< $num_needle_pats; $i++) { $needle_pat = mysql_fetch_array($needle_pats); echo "<div class=container>"; echo "<div class=name>".substr($needle_pat["LastName"],
0 , 3).", ".substr($needle_pat["FirstName"],
0, 2)."</div>"; $query
= "select distinct t1.sessionnumber, t2.description as location, t1.event, t3.description, t1.eventtime from lightpakeventtable as t1, lanpaktable
as t2, systemeventtable
as t3, sessiontable as t4, persontable
as t5 where t1.lanpakindex=t2.lanpakindex and
t1.event=t3.systemevent and t1.sessionnumber=t4.sessionnumber
and t4.personindex=t5.personindex and t5.patienttableindex=".$needle_pat["PatientTableIndex"]." and t1.event in (2,1,3,16,6,7,21,22)"; $needle_pats_info = mysql_query($query); $num_needle_pats_info = mysql_num_rows($needle_pats_info); echo "<table width=100% cellspacing=0>"; $alternate=0; $location=NULL; //for
loop prints out info for each needle loc patient for ($j=0; $j < $num_needle_pats_info;
$j++) { $needle_pat_info = mysql_fetch_array($needle_pats_info); if ( $location == $needle_pat_info["location"]
&& $description == $needle_pat_info["description"]
) else { if ($alternate == 0) { echo "<tr
class=d0>"; $alternate++; } else { echo "<tr
class=d1>"; $alternate--; } echo "<td class=location>".$needle_pat_info["location"]."</td>"; echo "<td class=event>".$needle_pat_info["description"]."</td>"; echo "<td class=time>".$needle_pat_info["eventtime"]."</td></tr>"; } $location
= $needle_pat_info["location"]; $description
= $needle_pat_info["description"]; } echo "</table>"; $location
= null; $description
= null; echo "</div><p>"; } ?> </body> </html> Appendix 2 The PHP script used for sequence
alignment. <html> <head> <title>Sequence Alignment</title> </head> <body> <link
rel="stylesheet" href="style.css"
type="text/css"> <?php function display_all($array,
$data, $template){ echo "<table
border=1>"; echo "<tr><td></td><td></td>"; foreach($data as $spotdeux){ echo "<td
align=center>".$spotdeux."</td>";
} echo "</tr>"; $i=-1; foreach($array as $spot){ echo "<tr><td>"; if ($i > -1) echo $template[$i]; echo "</td>"; foreach($spot as $spotdeux){ echo
"<td>".$spotdeux."</td>";
} echo "</tr>"; $i++; } echo
"</table>"; } function find_best($x, $y, $array,
$matrix, $data, $template, $new_data, $new_template){ if (($x == 0) && ($y == 0)){ echo "<br>".$array."<br>".count($array)."<br>"; $size_best=count($array); $array_final = array_reverse($array); echo "<table border=1><tr>"; for ($s = 0; $s < $size_best;
$s++){ echo "<td>".$array_final[$s]."</td>"; } echo "</tr><tr><td></td>"; $data_final = array_reverse($new_data); for ($s = 0; $s < count($new_data);
$s++){ echo "<td>".$data_final[$s]."</td>"; } echo "</tr><tr><td></td>"; $template_final = array_reverse($new_template); for ($s = 0; $s < count($new_template);
$s++){ echo "<td>".$template_final[$s]."</td>"; } echo "</tr></table>"; return $array; } else{ if (($x != 0) && ($y != 0))
$moves[]=$matrix[$x-1][$y-1]; if ($y != 0) $moves[]=$matrix[$x][$y-1]; if ($x != 0) $moves[]=$matrix[$x-1][$y]; echo "<br>".$x."
".$y." ".$array; $best_move=min($moves); if ($best_move ==
$matrix[$x-1][$y-1]){ $array[] = $matrix[$x-1][$y-1]; $new_template[]=$template[$x-1]; $new_data[]=$data[$y-1]; find_best($x-1,
$y-1, $array, $matrix, $data, $template, $new_data,
$new_template);} elseif ($best_move
== $matrix[$x][$y-1]){ $array[]=$matrix[$x][$y-1]; $new_template[]='-'; $new_data[]=$data[$y-1]; find_best($x,
$y-1, $array, $matrix, $data, $template, $new_data,
$new_template);} elseif ($best_move
== $matrix[$x-1][$y]){ $array[]=$matrix[$x-1][$y]; $new_template[]=$template[$x-1]; $new_data[]='-'; find_best($x-1,
$y, $array, $matrix, $data, $template, $new_data,
$new_template);} else } } $data=array('chk', 'wt', 'chng', 'chk', 'tt', 'trha',
'htrha', 'rnst', 'trha', 'sdsur', 'sdsur', 'sdsun', 'sdsur', 'sdsun', 'sdsur', 'rwt', 'rwt', 'exam', 'exam', 'exam', 'hall', 'trha',
'htrha',
'trha', 'tt', 'tr', 'htrha', 'sdsur', 'sdsun'); $template=array('chk', 'wt', 'chng', 'sdsur', 'hall', 'exam', 'hall', 'trha',
'tr', 'sdsur'); $size_data=count($data); $size_template=count($template); $g=1; $d=1; $matrix[0][0]=0; for($x = 1; $x < ($size_data +
1); $x++){ $matrix[0][$x]=$matrix[0][$x-1] + $g; echo "matrix[0][".$x."] = ".$matrix[0][$x]."<br>"; } for($y = 1; $y < ($size_template
+ 1); $y++){ $matrix[$y][0]=$matrix[$y-1][0] + $g; echo "matrix[".$y."][0] = ".$matrix[$y][0]."<br>"; } for($w = 1; $w < ($size_template
+ 1); $w++){ for ($v = 1; $v < ($size_data +
1); $v++){ if($template[$w-1] == $data[$v-1]){ echo "template[".($w-1)."] = ".$template[$w-1]." and data[".($v- 1)."] = ".$data[$v-1]."<br>"; echo "Compare ".$matrix[$w- 1][$v-1]." ".($matrix[$w-1][$v]
+ $g)." ".($matrix[$w][$v-1] + $g)."<br>"; $matrix[$w][$v]=min($matrix[$w-1][$v-1], ($matrix[$w-1][$v]
+ $g), ($matrix[$w][$v- 1] + $g)); } else{ echo "template[".($w-1)."] = ".$template[$w-1]." and data[".($v- 1)."] = ".$data[$v-1]."<br>"; echo "Compare ".($matrix[$w- 1][$v-1] + $d)." ".($matrix[$w- 1][$v] + $g)." ".($matrix[$w][$v-1]
+ $g)."<br>"; $matrix[$w][$v]=min(($matrix[$w-1][$v-1] + $d), ($matrix[$w-1][$v] + $g), ($matrix[$w][$v-1]
+ $g)); } } } display_all($matrix,
$data, $template); $initial=array($matrix[$size_template-1][$size_data-1]); $best_sequence = find_best($size_template, $size_data, $initial, $matrix, $data, $template, array(), array()); $size_best=count($best_sequence); echo $best_sequence."<br>"; echo $size_best."<br>"; for ($s = 0; $s < $size_best;
$s++){ echo $s." "; } ?> </body> </html> | |
